Single-cell RNA-seq data analysis

# No encoding supplied: defaulting to UTF-8 ?

# R Options
options(stringsAsFactors=FALSE,
        citation_format="pandoc", 
        dplyr.summarise.inform=FALSE, 
        knitr.table.format="html",
        kableExtra_view_html=TRUE,
        future.globals.maxSize=2000000000, mc.cores=4, 
        future.fork.enable=TRUE, future.plan="multicore",
        future.rng.onMisuse="ignore")

# Python3 needed for clustering, umap, other python packages
# Path to binary will be automatically found
# Set manually if it does not work
reticulate_python3_path = unname(Sys.which("python3"))
Sys.setenv(RETICULATE_PYTHON = reticulate_python3_path)

# Required libraries
library(Seurat) # main
library(ggplot2) # plots
library(patchwork) # combination of plots
library(magrittr) # %>% operator
library(reticulate) # required for 'leiden' clustering
library(enrichR) # functional enrichment
library(future) # multicore support for Seurat

# Other libraries we use
# Knit: knitr
# Data handling: dplyr, tidyr, purrr, stringr, Matrix, sctransform
# Tables: kableExtra, DT
# Plots: ggsci, ggpubr
# IO: openxlsx, readr, R.utils
# Annotation: biomaRt
# DEG: mast
# Functional enrichment: enrichR
# Other: sessioninfo, cerebroApp

# Knitr default options
knitr::opts_chunk$set(echo=TRUE,                     # output code
                      cache=FALSE,                   # do not cache results
                      message=TRUE,                  # show messages
                      warning=TRUE,                  # show warnings
                      tidy=FALSE,                    # do not auto-tidy-up code
                      fig.width=10,                  # default fig width in inches
                      class.source='fold-hide',      # by default collapse code blocks
                      dev=c('png', 'pdf'),           # create figures in png and pdf; the first device (png) will be used for HTML output
                      dev.args=list(png=list(type="cairo"),  # png: use cairo - works on cluster, supports anti-aliasing (more smooth)
                                    pdf=list(bg="white")),     # pdf: use cairo - works on cluster, supports anti-aliasing (more smooth)
                      dpi=96                         # figure resolution
                                     
)

Dataset description

• 10x and Smartseq2 datasets of PBMC cells
• Taken from “Systematic comparative analysis of single cell RNA-sequencing methods” Liu et al. (2019)

Project-specific parameters

This code chunk contains all parameters that are set specifically for the project.

param = list()

####################
# Input parameters #
####################
# Project ID
param$project_id = "pbmc"

# Path to input data
param$path_data = data.frame(name=c("pbmc_10x","pbmc_smartseq2"),
                             type=c("10x","smartseq2"),
                             path=c("test_datasets/10x_SmartSeq2_pbmc_GSE132044/counts/10x/", "test_datasets/10x_SmartSeq2_pbmc_GSE132044/counts/smartseq2/counts_table.tsv.gz"),  
                             stats=c(NA, NA))

#param$path_data = data.frame(name="pbmc_10x",
#                             type="10x",
#                             path="test_datasets/10x_SmartSeq2_pbmc_GSE132044/counts/10x/",
#                             stats=NA)

# Downsample data to at most n cells per sample (mainly for tests)
#   NULL to deactivate
param$downsample_cells_n = NULL

# Path to output directory
param$path_out = "test_datasets/10x_SmartSeq2_pbmc_GSE132044/results/"

# Marker genes based on literature, translated to Ensembl IDs
#   xlsx file, one list per column, first row as header and Ensembl IDs below
#   NULL if no known marker genes should be plotted
param$file_known_markers = "test_datasets/10x_pbmc_1k_healthyDonor_v3Chemistry/known_markers.xlsx"

# Annotation via biomaRt
param$mart_dataset = "hsapiens_gene_ensembl"
param$annot_version = 98
param$annot_main = c(ensembl="ensembl_gene_id", symbol="external_gene_name", entrez="entrezgene_accession")
param$mart_attributes = c(param$annot_main, 
                          c("chromosome_name", "start_position", "end_position", 
                            "percentage_gene_gc_content", "gene_biotype", "strand", "description"))
param$biomart_mirror = NULL

# Alternatively, we can read a previously compiled annotation table from file
param$file_annot = NULL

# Prefix for mitochondrial genes 
param$mt = "^MT-"

#####################
# Filter parameters #
#####################
# Filter for cells
param$cell_filter = list(nFeature_RNA=c(200, NA), percent_mt=c(NA, 20))

# Filter for features
param$feature_filter = list(min_counts=1, min_cells=3) # feature has to be found by at least one count in one cell

# Samples to drop
# Cells from these samples will be dropped after initial QC
# Example: param$samples_to_drop = c("pbmc_smartseq2_NC", "pbmc_smartseq2_RNA"), 
#   where "pbmc_smartseq2" is the name of the dataset, and "NC" and "RNA" are the names of the subsamples
param$samples_to_drop = c() 

# Drop samples with too few cells
param$samples_min_cells = 10

############################
# Normalisation parameters #
############################
# Which normalisation should be used for analysis?
#   "RNA" or "SCT"
param$norm = "RNA"

# Whether or not to remove cell cycle effects
param$cc_remove = FALSE

# Should all cell cycle effects be removed, or only the difference between profilerating cells (G2M and S phase)?
# Read https://satijalab.org/seurat/v3.1/cell_cycle_vignette.html, for an explanation
param$cc_remove_all = FALSE

# Whether or not to re-score cell cycle effects after data
#   from different samples have been merged 
param$cc_rescore_after_merge = TRUE

# Additional (unwanted) variables that will be regressed out for visualisation and clustering
param$vars_to_regress = c()

# When there are multiple datasets, how to combine them:
#   - method:
#     - "single": Default when there is only one dataset after filtering, no integration is needed
#     - "merge": Merge (in other words, concatenate) data when no integration is needed, e.g. when samples were multiplexed on the same chip
#     - "standard": Integration: Anchors are computed for all pairs of datasets
#                     This will give all datasets the same weight during dataset integration but can be computationally intensive
#     - "reference": Integration: One dataset is used as reference and anchors are computed for all other datasets
#                      Less accurate but computationally faster 
#                      Not implemented yet
#     - "reciprocal": Integration: Anchors are computed in PCA space instead of the data
#                       Even less accurate but for very big datasets
#   - reference_dataset: When using method="reference", which dataset is the reference? 
#                          Can be numeric or name of the dataset
#   - dimensions: Number of dimensions to consider for integration
param$integrate_samples = list(method="standard", reference_dataset=1, dimensions=30)

# The number of PCs to use; adjust this parameter based on the Elbowplot 
param$pc_n = 10

# Resolution of clusters; low values will lead to fewer clusters of cells 
param$cluster_resolution=0.5

#######################################################
# Marker genes and genes with differential expression #
#######################################################
# Thresholds to define marker genes
param$marker_padj = 0.05
param$marker_log2FC = log2(2)
param$marker_pct = 0.25

# Additional (unwanted) variables to account for in statistical tests
param$latent_vars = c()

# Contrasts to find differentially expressed genes (R data.frame or Excel file)
# Required columns:
# condition_column: Categorial column in the cell metadata; specify "orig.ident" for sample and "seurat_clusters" for cluster
# condition_group1: Condition levels in group 1, multiple levels concatenated by the plus character
#                     Empty string = all levels not in group2 (cannot be used if group2 is empty)
# condition_group2: Condition levels in group 2, multiple levels concatenated by the plus character
#                     Empty string = all levels not in group1 (cannot be used if group1 is empty)
#
# Optional columns:
# subset_column: Categorial column in the cell metadata to subset before testing (default: NA)
#                  Specify "orig.ident" for sample and "seurat_clusters" for cluster 
# subset_group: Further subset levels (default: NA)
#                 For the individual analysis of multiple levels separate by semicolons
#                 For the joint analysis of multiple levels concatenate by the plus character 
#                 For the individual analysis of all levels empty string ""
# assay: Seurat assay to test on; can also be a Seurat dimensionality reduction (default: "RNA")
# slot: In case assay is a Seurat assay object, which slot to use (default: "data")
# padj: Maximum adjusted p-value (default: 0.05)
# log2FC: Minimum absolute log2 fold change (default: 0)
# min_pct: Minimum percentage of cells expressing a gene to test (default: 0.1)
# test: Type of test; "wilcox", "bimod", "roc", "t", "negbinom", "poisson", "LR", "MAST", "DESeq2"; (default: "wilcox")
# latent_vars: Additional variables to account for; multiple variables need to be concatenated by semicolons; will overwrite the default by param$latent_vars (default: none).
param$deg_contrasts = data.frame(condition_column=c("orig.ident", "orig.ident", "Phase"),
                                 condition_group1=c("pbmc_10x", "pbmc_10x", "G1"),
                                 condition_group2=c("pbmc_smartseq2_sample1", "pbmc_smartseq2_sample1", "G2M"),
                                 subset_column=c(NA, "seurat_clusters", "seurat_clusters"),
                                 subset_group=c(NA, "", "1;2"))

# P-value threshold for functional enrichment tests
param$enrichr_padj = 0.05

# Enrichr databases of interest
param$enrichr_dbs = c("GO_Molecular_Function_2018", "GO_Biological_Process_2018", "GO_Cellular_Component_2018")

######################
# General parameters #
######################
# Main colour to use for plots
param$col = "palevioletred"

# Colour palette and colours used for samples
param$col_palette_samples = "ggsci::pal_jama"

# Colour palette and colours used for cluster
param$col_palette_clusters = "ggsci::pal_startrek"

# Path to git repository
param$path_to_git = "."

# Debugging mode: 
# 'default_debugging' for default, 'terminal_debugger' for debugging without X11, 'print_traceback' for non-interactive sessions 
param$debugging_mode = "default_debugging"

# Path for figures in png and pdf format
knitr::opts_chunk$set(fig.path=paste(param$path_out, "figures/", sep="/"))

# Git directory and files to source must be done first, then all helper functions can be sourced
git_files_to_source = c("R/functions_io.R",
              "R/functions_plotting.R",
              "R/functions_analysis.R",
              "R/functions_degs.R",
              "R/functions_util.R")
git_files_to_source = paste(param$path_to_git, git_files_to_source, sep="/")
file_exists = purrr::map_lgl(git_files_to_source, file.exists)
if (any(!file_exists)) stop(paste("The following files could not be found:",paste(git_files_to_source[!file_exists], collapse=", "), ". Please check the git directory at '", param$path_to_git, "'.!"))
invisible(purrr::map(git_files_to_source, source))

# Debugging mode: 
# 'default_debugging' for default, 'terminal_debugger' for debugging without X11, 'print_traceback' for non-interactive sessions 
switch (param$debugging_mode, 
        default_debugging=on_error_default_debugging(), 
        terminal_debugger=on_error_start_terminal_debugger(),
        print_traceback=on_error_just_print_traceback(),
        on_error_default_debugging())

# Set output hooks
knitr::knit_hooks$set(message=format_message, warning=format_warning)

# Create output directories
if (!file.exists(param$path_out)) dir.create(param$path_out, recursive=TRUE, showWarnings=FALSE)
#dir.create(paste(param$path_out, "images", sep="/"), recursive=TRUE, showWarnings=FALSE)
#dir.create(paste(param$path_out, "marker", sep="/"), recursive=TRUE, showWarnings=FALSE)
#dir.create(paste(param$path_out, "degs", sep="/"), recursive=TRUE, showWarnings=FALSE)

# Do checks
error_messages = c()

# Check installed packages
error_messages = c(error_messages, check_installed_packages())
# Check python
error_messages = c(error_messages, check_python())
# Check parameters
error_messages = c(error_messages, check_parameters(param))
# Check enrichR
error_messages = c(error_messages, check_enrichr(param$enrichr_dbs))
# Check ensembl
error_messages = c(error_messages, check_ensembl(biomart="ensembl", 
                                                 dataset=param$mart_dataset, 
                                                 mirror=param$biomart_mirror, 
                                                 version=param$annot_version,
                                                 attributes=param$mart_attributes))

Read data

Read and print mapping statistics

We begin by printing mapping statistics that have been produced prior to this workflow.

# Are statistics provided?
if (!all(is.na(param$path_data$stats))) { 
  
  # Loop through all samples and read mapping stats
  mapping_stats_list = list()
  for (i in 1:nrow(param$path_data)) {  
    if (!is.na(param$path_data$stats[i])) { 
      mapping_stats_list[[param$path_data$name[i]]] = read.delim(param$path_data$stats[i], 
                                                                 sep=",", header=FALSE, check.names=FALSE) %>%
        t() %>% as.data.frame()
    } 
  }
  
  # Join all mapping stats tables
  mapping_stats = mapping_stats_list %>% purrr::reduce(dplyr::full_join, by="V1")
  rownames(mapping_stats) = mapping_stats[["V1"]]
  mapping_stats = mapping_stats %>% dplyr::select(-V1)
  colnames(mapping_stats) = names(mapping_stats_list)
 
  # Print table to HTML 
  knitr::kable(mapping_stats, align="l", caption="Mapping statistics") %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))
 
} else { 
  message("Mapping statistics cannot be shown. No valid file provided.")
}

× (Message)
Mapping statistics cannot be shown. No valid file provided.

Read gene annotation

If not provided already, we read gene annotation from Ensembl and write the resulting table to file. We generate several dictionaries to translate between Ensembl IDs, gene symbols, Entrez Ids, and Seurat rownames.

# If not provided by user, save annotation in the path_out directory
if (is.null(param$file_annot)) {
  param$file_annot = file.path(param$path_out, paste0(param$mart_dataset, ".v", param$annot_version, ".annot.txt"))
}

# Read annotation from csv or from Ensembl and a tab separated txt will be created
if (file.exists(param$file_annot)) {
  annot_ensembl = read.delim(param$file_annot)
} else {
  annot_mart = suppressWarnings(GetBiomaRt(biomart="ensembl", 
                                           dataset=param$mart_dataset, 
                                           mirror=param$biomart_mirror, 
                                           version=param$annot_version))
  annot_ensembl = biomaRt::getBM(mart=annot_mart, attributes=param$mart_attributes, useCache=FALSE)
  write.table(annot_ensembl, file=param$file_annot, sep='\t', col.names=TRUE, row.names=FALSE, append=FALSE)
  message("Gene annotation file was created at: ", param$file_annot)
  # Note: depending on the attributes, there might be more than one row per gene
}

# Double-check if we got all required annotation, in case annotation file was read
check_annot_main = all(param$annot_main %in% colnames(annot_ensembl))
if (!check_annot_main) {
  stop("The annotation table misses at least one of the following columns: ", paste(param$annot_main, collapse=", "))
}

# Create translation tables
ensembl = param$annot_main["ensembl"]
symbol = param$annot_main["symbol"]
entrez = param$annot_main["entrez"]

# Ensembl id to gene symbol
ensembl_to_symbol = unique(annot_ensembl[, c(ensembl, symbol)])
ensembl_to_symbol = setNames(ensembl_to_symbol[, symbol], ensembl_to_symbol[, ensembl])

# Ensembl id to seurat-compatible unique rowname
ensembl_to_seurat_rowname = unique(annot_ensembl[, c(ensembl, symbol)])
ensembl_to_seurat_rowname[, symbol] = make.unique(gsub(pattern="_", replacement="-", x=ensembl_to_seurat_rowname[, symbol], fixed=TRUE))
ensembl_to_seurat_rowname = setNames(ensembl_to_seurat_rowname[, symbol], ensembl_to_seurat_rowname[, ensembl])

# Seurat-compatible unique rowname to ensembl id
seurat_rowname_to_ensembl = setNames(names(ensembl_to_seurat_rowname), ensembl_to_seurat_rowname)

# Gene symbol to ensembl id: named LIST to account for genes where one symbol translates to multiple Ensembl IDs
symbol_to_ensembl_df = unique(annot_ensembl[, c(ensembl, symbol)])
symbol_to_ensembl = split(symbol_to_ensembl_df[, ensembl], symbol_to_ensembl_df[, symbol])

# Gene symbol to (seurat compatible unique) gene symbol: named LIST to account for genes with multiple names
symbol_to_seurat_rowname = unique(annot_ensembl[, c(ensembl, symbol)])
symbol_to_seurat_rowname$seurat_rowname = ensembl_to_seurat_rowname[symbol_to_seurat_rowname[, ensembl]]
symbol_to_seurat_rowname = split(symbol_to_seurat_rowname$seurat_rowname, symbol_to_seurat_rowname[, symbol])

# Ensembl to Entrez
ensembl_to_entrez = unique(annot_ensembl[, c(ensembl, entrez)])
ensembl_to_entrez[, entrez] = ifelse(nchar(ensembl_to_entrez[, entrez]) == 0, NA, ensembl_to_entrez[, entrez])
ensembl_to_entrez = split(ensembl_to_entrez[, entrez], ensembl_to_entrez[, ensembl])

# Seurat-compatible unique rowname to Entrez
seurat_rowname_to_ensembl_match = match(seurat_rowname_to_ensembl, names(ensembl_to_entrez))
names(seurat_rowname_to_ensembl_match) = names(seurat_rowname_to_ensembl)
seurat_rowname_to_entrez = purrr::map(seurat_rowname_to_ensembl_match, function(i) {unname(ensembl_to_entrez[[i]])})

# Entrez IDs is duplicating Ensembl IDs in annot_ensembl
# Therefore, we remove Entrez IDs from the annotation table, after generating all required translation tables
# Set rownames of annotation table to Ensembl identifiers
annot_ensembl = annot_ensembl[, -match(entrez, colnames(annot_ensembl))] %>% unique() %>% as.data.frame()
rownames(annot_ensembl) = annot_ensembl[, ensembl]

# Use biomart to translate human cell cycle genes to the species of interest and save them in a file
cc_genes_marker_file = paste0(param$path_out, "/cell_cycle_markers.xlsx")

if (file.exists(cc_genes_marker_file)) {
  # Load from file
  genes_s = openxlsx::read.xlsx(cc_genes_marker_file, sheet=1)
  genes_g2m = openxlsx::read.xlsx(cc_genes_marker_file, sheet=2)
  
} else { 
  # Obtain from Ensembl
  # Note: both mart objects must point to the same mirror for biomarT::getLDS to work
  mart_human = suppressWarnings(GetBiomaRt(biomart="ensembl", 
                                           dataset="hsapiens_gene_ensembl", 
                                           mirror=param$biomart_mirror, 
                                           version=param$annot_version))
  mart_myspecies = suppressWarnings(GetBiomaRt(biomart="ensembl", 
                                               dataset=param$mart_dataset, 
                                               mirror=GetBiomaRtMirror(mart_human), 
                                               version=param$annot_version)) 
  
  # S phase marker
  genes_s = biomaRt::getLDS(attributes=c("ensembl_gene_id", "external_gene_name"), 
                          filters="external_gene_name", 
                          values=Seurat::cc.genes.updated.2019$s.genes, 
                          mart=mart_human, 
                          attributesL=c("ensembl_gene_id", "external_gene_name"), 
                          martL=mart_myspecies, 
                          uniqueRows=TRUE)
  colnames(genes_s) = c("Human_ensembl_id", "Human_gene_name", "Species_ensembl_id", "Species_gene_name")
  
  # G2/M marker
  genes_g2m = biomaRt::getLDS(attributes=c("ensembl_gene_id", "external_gene_name"), 
                            filters="external_gene_name", 
                            values=Seurat::cc.genes.updated.2019$g2m.genes, 
                            mart=mart_human, 
                            attributesL=c("ensembl_gene_id", "external_gene_name"), 
                            martL=mart_myspecies, 
                            uniqueRows=TRUE)
  colnames(genes_g2m) = c("Human_ensembl_id", "Human_gene_name", "Species_ensembl_id", "Species_gene_name")
  
  # Write to file
  openxlsx::write.xlsx(list(S_phase=genes_s,G2M_phase=genes_g2m),file=cc_genes_marker_file)
}

# Convert Ensembl ID to Seurat-compatible unique rowname
genes_s = data.frame(Human_gene_name=genes_s$Human_gene_name, Species_gene_name=unname(ensembl_to_seurat_rowname[genes_s$Species_ensembl_id]))
genes_g2m = data.frame(Human_gene_name=genes_g2m$Human_gene_name, Species_gene_name=unname(ensembl_to_seurat_rowname[genes_g2m$Species_ensembl_id]))

Read scRNA-seq data

We next read the scRNA-seq dataset(s) into Seurat.

# List of Seurat objects
sc = list()

datasets = param$path_data
for (i in seq(nrow(datasets))) {
  name = datasets[i,"name"]
  type = datasets[i,"type"]
  path = datasets[i,"path"]
  
  # Read 10X or smartseq2
  if (type == "10x") {
    # Read 10X sparse matrix into a Seurat object
    sc = c(sc, ReadSparseMatrix(path, 
                                  project=name, 
                                  row_name_column=1, 
                                  convert_row_names=ensembl_to_seurat_rowname))
    
  } else if (type == "smartseq2") {
    # Read counts table into a Seurat object
    sc = c(sc, ReadCountsTable(path, project=name, row_name_column=1, convert_row_names=ensembl_to_seurat_rowname, parse_plate_information=TRUE, return_samples_as_datasets=TRUE))
  } 
}

# Make sure that sample names are unique. If not, just prefix with the dataset name. Also set orig.ident to this name.
sample_names = names(sc)
duplicated_sample_names_idx = which(sample_names %in% sample_names[duplicated(sample_names)])
for (i in duplicated_sample_names_idx) {
  sample_names[i] = paste(head(sc[[i]][["orig.dataset", drop=TRUE]],1), sample_names[i], sep=".")
  sc[[i]][["orig.ident"]] = sample_names[i]
}

# Make cell names unique
sc = purrr::map(list_indices(sc), function(i){
  cell_names = gsub("-\\d+", "", colnames(sc[[i]]))
  Seurat::RenameCells(sc[[i]], new.names=paste(cell_names, i, sep="-"))
})

# Set up colors for samples
sample_names = purrr::flatten_chr(purrr::map(sc, function(s){ unique(as.character(s[[]][["orig.ident"]])) }))
param$col_samples = GenerateColours(num_colours=length(sample_names), palette=param$col_palette_samples)
names(param$col_samples) = sample_names

# Downsample cells if requested
if (!is.null(param$downsample_cells_n)) {
  sc = purrr::map(sc, function(s) {
    cells = ScSampleCells(sc=s, n=param$downsample_cells_n)
    return(subset(s, cells=cells))
  })
}

sc

## $pbmc_10x
## An object of class Seurat 
## 33694 features across 4033 samples within 1 assay 
## Active assay: RNA (33694 features, 0 variable features)
## 
## $pbmc_smartseq2_sample1
## An object of class Seurat 
## 33694 features across 311 samples within 1 assay 
## Active assay: RNA (33694 features, 0 variable features)

The following first table shows metadata (columns) of the first 5 cells (rows). These metadata provide additional information about the cells in the dataset, such as the sample a cell belongs to (“orig.ident”), or the above mentioned number of unique genes detected (“nFeature_RNA”). The second table shows metadata (columns) of the first 5 genes (rows).

# Combine cell metadata of the Seurat objects into one big metadata
sc_cell_metadata = suppressWarnings(purrr::map_dfr(sc, function(s){ s[[]] }) %>% as.data.frame())

# Print cell metadata
knitr::kable(head(sc_cell_metadata), align="l", caption="Cell metadata, top 5 rows") %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))  %>% 
  kableExtra::scroll_box(width="100%")

Cell metadata, top 5 rows

	orig.ident	nCount_RNA	nFeature_RNA	orig.dataset	SampleName	PlateNumber	PlateRow	PlateCol
PBMC1_10x_AAACCCACACTTGGGC-1	pbmc_10x	7552	2037	pbmc_10x	NA	NA	NA	NA
PBMC1_10x_AAACCCACAGGTGGAT-1	pbmc_10x	4773	1800	pbmc_10x	NA	NA	NA	NA
PBMC1_10x_AAACCCAGTGCTTATG-1	pbmc_10x	4430	1565	pbmc_10x	NA	NA	NA	NA
PBMC1_10x_AAACCCATCCGTAGTA-1	pbmc_10x	4512	1592	pbmc_10x	NA	NA	NA	NA
PBMC1_10x_AAACCCATCTTACACT-1	pbmc_10x	6663	1919	pbmc_10x	NA	NA	NA	NA
PBMC1_10x_AAACGAAGTCTAGTGT-1	pbmc_10x	143	89	pbmc_10x	NA	NA	NA	NA

# Print gene metadata
knitr::kable(head(sc[[1]][["RNA"]][[]], 5), align="l", caption="Feature metadata, top 5 rows (only first dataset shown)") %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))  %>% 
  kableExtra::scroll_box(width="100%")

Feature metadata, top 5 rows (only first dataset shown)

	feature_id	feature_name	feature_type
TSPAN6	ENSG00000000003	TSPAN6	Gene Expression
TNMD	ENSG00000000005	TNMD	Gene Expression
DPM1	ENSG00000000419	DPM1	Gene Expression
SCYL3	ENSG00000000457	SCYL3	Gene Expression
C1orf112	ENSG00000000460	C1orf112	Gene Expression

Pre-processing

Quality control

We start the analysis by removing unwanted cells from the dataset(s). Three commonly used QC metrics include the number of unique genes detected in each cell (“nFeature_RNA”), the total number of molecules detected in each cell (“nCount_RNA”), and the percentage of counts that map to the mitochrondrial genome (“percent_mt”). If ERCC spike-in controls were used, the percentage of counts mapping to them is also shown (“percent_ercc”).

# If filters were specified globally (i.e. not by sample), this chunk will copy them for each sample such that downstream filtering can work by sample
param$cell_filter = purrr::map(list_names(sc), function(s) {
  if (s %in% names(param$cell_filter)) {
    return(param$cell_filter[[s]])
  } else {
    return(param$cell_filter)
  }
})

param$feature_filter = purrr::map(list_names(sc), function(s) {
  if (s %in% names(param$feature_filter)) {
    return(param$feature_filter[[s]])
  } else {
    return(param$feature_filter)
  }
})

# Calculate percentage of counts in mitochondrial genes for each Seurat object
sc = purrr::map(sc, Seurat::PercentageFeatureSet, pattern=param$mt, col.name="percent_mt", assay="RNA")

# Calculate percentage of counts in ERCC for each Seurat object (if assay is available)
sc = purrr::map(sc, function(s) {
  if ("ERCC" %in% Seurat::Assays(s)) s$percent_ercc = s$nCount_ERCC/(s$nCount_ERCC + s$nCount_RNA)*100
  return(s)
  })

# Combine (again) cell metadata of the Seurat objects into one big metadata, this time including mt and ercc 
sc_cell_metadata = suppressWarnings(purrr::map_dfr(sc, function(s){ s[[]] }) %>% as.data.frame())

# Only RNA assay at the moment
# counts_median uses sapply on the counts matrix, which converts the sparse matrix into a normal matrix
#   This might have to be adapted in future (Sparse Matrix Apply Function)
sc = purrr::map(list_names(sc), function(n) {
  # Calculate percentage of counts per gene in a cell
  counts_rna = Seurat::GetAssayData(sc[[n]], slot="counts", assay="RNA")
  total_counts = sc[[n]][["nCount_RNA", drop=TRUE]]
  counts_rna_perc = Matrix::t(Matrix::t(counts_rna)/total_counts)*100

  # Calculate feature filters
  num_cells_expr = Matrix::rowSums(counts_rna >= 1)
  num_cells_expr_threshold = Matrix::rowSums(counts_rna >= param$feature_filter[[n]][["min_counts"]])
  
  # Calculate median of counts_rna_perc per gene 
  counts_median = apply(counts_rna_perc, 1, median)
  
  # Add all QC measures as metadata
  sc[[n]][["RNA"]] = Seurat::AddMetaData(sc[[n]][["RNA"]], data.frame(num_cells_expr, num_cells_expr_threshold, counts_median))
  return(sc[[n]])
})

# Plot QC metrics for cells
cell_qc_features = c("nFeature_RNA", "nCount_RNA", "percent_mt")
if ("percent_ercc" %in% colnames(sc_cell_metadata)) cell_qc_features = c(cell_qc_features, "percent_ercc")
cell_qc_features = values_to_names(cell_qc_features)

p_list = list()
for (i in names(cell_qc_features)) {
  p_list[[i]]= ggplot(sc_cell_metadata[, c("orig.ident", i)], aes_string(x="orig.ident", y=i, fill="orig.ident")) +
    geom_violin(scale="width")

  # Adds points for samples with less than three cells since geom_violin does not work here
  p_list[[i]] = p_list[[i]] + 
    geom_point(data=sc_cell_metadata[, c("orig.ident", i)] %>% dplyr::filter(orig.ident %in% names(which(table(sc_cell_metadata$orig.ident) < 3))), aes_string(x="orig.ident", y=i, fill="orig.ident"), shape=21, size=2)
  
  # Now add styles
  p_list[[i]] = p_list[[i]] + 
    AddStyle(title=i, legend_position="none", fill=param$col_samples, xlab="") + 
    theme(axis.text.x=element_text(angle=45, hjust=1))
  
  # Creates a table with min/max values for filter i for each dataset
  cell_filter_for_plot = purrr::map_dfr(names(param$cell_filter), function(n) {
    # If filter i in cell filter of the dataset, then create dataframe with columns orig.ident, threshold and value
    if (i %in% names(param$cell_filter[[n]])){
      data.frame(orig.ident=n, threshold=c("min", "max"), value=param$cell_filter[[n]][[i]], stringsAsFactors=FALSE)
    } 
  })
  
  # Add filters as segments to plot
  if (nrow(cell_filter_for_plot) > 0) {
    # Remove entries that are NA
    cell_filter_for_plot = cell_filter_for_plot %>% dplyr::filter(!is.na(value))
    p_list[[i]] = p_list[[i]] + geom_segment(data=cell_filter_for_plot, 
                                             aes(x=as.integer(as.factor(orig.ident))-0.5, 
                                                 xend=as.integer(as.factor(orig.ident))+0.5, 
                                                 y=value, yend=value), 
                                             lty=2, col="firebrick")
  }
}
p = patchwork::wrap_plots(p_list, ncol=2) + patchwork::plot_annotation("Distribution of feature values") 
p

# Correlate QC metrics for cells
p_list = list()
p_list[[1]] = ggplot(sc_cell_metadata, aes_string(x=cell_qc_features[2], y=cell_qc_features[1], colour="orig.ident")) +
  geom_point() + 
  AddStyle(col=param$col_samples)
p_list[[2]] = ggplot(sc_cell_metadata, aes_string(x=cell_qc_features[3], y=cell_qc_features[1], colour="orig.ident")) +
  geom_point() + 
  AddStyle(col=param$col_samples)
p = patchwork::wrap_plots(p_list, ncol=2) + patchwork::plot_annotation("Features plotted against each other")

if (length(sc)==1) {
  p = p & theme(legend.position="bottom")
} else {
  p = p + patchwork::plot_layout(guides = "collect") & theme(legend.position="bottom")
}
p

Genes with highest expression

We next investigate whether there are individual genes that are represented by an unusually high number of counts. For each cell, we first calculate the percentage of counts per gene. Subsequently, for each gene, we calculate the median value of these percentages. Genes with the highest median percentage of counts are plotted below.

# Plot only samples that we intend to keep 
sc_names = names(sc)[!(names(sc) %in% param$samples_to_drop)]
genes_highestExpr = lapply(sc_names, function(i) {
  idx = sc[[i]][["RNA"]][["counts_median"]] %>% order(decreasing=TRUE) %>% head(n=10)
  return(rownames(sc[[i]][["RNA"]][[]])[idx])
  }) %>%
  unlist() %>%
  unique()

genes_highestExpr_counts = purrr::map_dfc(sc[sc_names], .f=function(s) s[["RNA"]][["counts_median"]][genes_highestExpr, ]) 
genes_highestExpr_counts$gene = genes_highestExpr

genes_highestExpr_counts = genes_highestExpr_counts %>% tidyr::pivot_longer(cols=all_of(sc_names))
genes_highestExpr_counts$name = factor(genes_highestExpr_counts$name, levels=sc_names)

col =  GenerateColours(num_colours=length(genes_highestExpr), palette="ggsci::pal_simpsons")
ggplot(genes_highestExpr_counts, aes(x=name, y=value, col=gene, group=gene)) + 
  geom_point() + 
  geom_line() + 
  AddStyle(title="Top 10 highest expressed genes per sample, added into one list", 
           xlab="Sample", ylab="Median % of raw counts\n per gene in a cell", 
           legend_position="bottom", 
           col=col)

Filtering

Cells and genes are filtered based on the following thresholds:

cell_filter_list = param$cell_filter %>% unlist(recursive=FALSE)
cell_filter_tbl = cell_filter_list %>% purrr::reduce(rbind) %>% as.data.frame()
rownames(cell_filter_tbl) = names(cell_filter_list)
colnames(cell_filter_tbl) = c("Min", "Max")

feature_filter_list = param$feature_filter %>% unlist(recursive=FALSE)
feature_filter_tbl = feature_filter_list %>% purrr::reduce(rbind) %>% as.data.frame()
rownames(feature_filter_tbl) = names(feature_filter_list)
colnames(feature_filter_tbl) = "n"

knitr::kable(cell_filter_tbl, align="l", caption="Filters applied to cells") %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))

Filters applied to cells

	Min	Max
pbmc_10x.nFeature_RNA	200	NA
pbmc_10x.percent_mt	NA	20
pbmc_smartseq2_sample1.nFeature_RNA	200	NA
pbmc_smartseq2_sample1.percent_mt	NA	20

knitr::kable(feature_filter_tbl, align="l", caption="Filters applied to genes") %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))

Filters applied to genes

	n
pbmc_10x.min_counts	1
pbmc_10x.min_cells	3
pbmc_smartseq2_sample1.min_counts	1
pbmc_smartseq2_sample1.min_cells	3

The number of excluded cells and features is as follows:

# Iterate over datasets and filters
# Record a cell if it does not pass the filter
# Also record a cell if it belongs to a sample that should be dropped
sc_cells_to_exclude  = purrr::map(list_names(sc), function(n) { 
  filter_result = purrr::map(list_names(param$cell_filter[[n]]), function(f) {
    filter = param$cell_filter[[n]][[f]]
    if (is.numeric(filter)) {
      if (is.na(filter[1])) filter[1] = -Inf # Minimum
      if (is.na(filter[2])) filter[2] = Inf  # Maximum 
      idx_exclude = sc[[n]][[f, drop=TRUE]] < filter[1] | sc[[n]][[f, drop=TRUE]] > filter[2]
      return(names(which(idx_exclude)))
    } else if (is.character(filter)) { 
      return(names(which(sc[[n]][[f, drop=TRUE]] %in% filter)))
    }
  })

  # Samples to drop
  if (n %in% param$samples_to_drop) {
    filter_result[["samples_to_drop"]] = colnames(sc[[n]])
  } else {
    filter_result[["samples_to_drop"]] = as.character(c())
  }
  
  # Minimum number of cells for a sample to keep
  if (ncol(sc[[n]]) < param$samples_min_cells) {
    filter_result[["samples_min_cells"]] = colnames(sc[[n]])
  } else {
    filter_result[["samples_min_cells"]] = as.character(c())
  }
  
  return(filter_result)
})

# Summarise
sc_cells_to_exclude_summary = purrr::map_dfr(sc_cells_to_exclude, function(s) {
  return(as.data.frame(purrr::map(s, length))) 
  })
rownames(sc_cells_to_exclude_summary) = names(sc_cells_to_exclude)
knitr::kable(sc_cells_to_exclude_summary, align="l", caption="Number of excluded cells") %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) 

Number of excluded cells

	nFeature_RNA	percent_mt	samples_to_drop	samples_min_cells
pbmc_10x	286	271	0	0
pbmc_smartseq2_sample1	1	0	0	0

# Now filter, drop the respective colours and adjust integration method
sc = purrr::map(list_names(sc), function(n) {
  cells_to_keep = Cells(sc[[n]])
  cells_to_keep = cells_to_keep[!cells_to_keep %in% purrr::flatten_chr(sc_cells_to_exclude[[n]])]
  if (length(cells_to_keep)==0) return(NULL)
  else return(subset(sc[[n]], cells=cells_to_keep))
}) %>% purrr::discard(is.null)

if (length(sc)==1) param$integrate_samples[["method"]] = "single"

# Only RNA assay at the moment

# Iterate over datasets and record a feature if it does not pass the filter
sc_features_to_exclude = purrr::map(list_names(sc), function(n) {
  if (length(Cells(sc[[n]])) < param$feature_filter[[n]][["min_cells"]]) return(list())
  else return(names(which(sc[[n]][["RNA"]][["num_cells_expr_threshold", drop=TRUE]] < param$feature_filter[[n]][["min_cells"]])))
})

# Summarise
sc_features_to_exclude_summary = purrr::map(sc_features_to_exclude, length) %>% 
  t() %>% as.data.frame() 
rownames(sc_features_to_exclude_summary) = c("Genes")
knitr::kable(sc_features_to_exclude_summary, align="l", caption="Number of excluded genes") %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))

Number of excluded genes

	pbmc_10x	pbmc_smartseq2_sample1
Genes	13893	15754

# Now filter
sc = purrr::map(list_names(sc), function(n) {
  assay_names = Seurat::Assays(sc[[n]])
  features_to_keep = purrr::map(values_to_names(assay_names), function(a) {
    features = rownames(sc[[n]][[a]])
    keep = features[!features %in% sc_features_to_exclude[[n]]]
    return(keep)
  })
  return(subset(sc[[n]], features=purrr::flatten_chr(features_to_keep)))
})

After filtering, the size of the Seurat object is:

sc

## $pbmc_10x
## An object of class Seurat 
## 19801 features across 3608 samples within 1 assay 
## Active assay: RNA (19801 features, 0 variable features)
## 
## $pbmc_smartseq2_sample1
## An object of class Seurat 
## 17940 features across 310 samples within 1 assay 
## Active assay: RNA (17940 features, 0 variable features)

Normalisation

In this section, we subsequently run a series of Seurat functions for each provided sample:
1. We start by running a standard log normalisation, where counts for each cell are divided by the total counts for that cell and multiplied by 10,000. This is then natural-log transformed.
2. We assign cell cycle scores to each cell based on its normalised expression of G2/M and S phase markers. These scores are visualised in a separate section further below. If specified in the above parameter section, cell cycle effects are removed during scaling (step 3).
3. Dependent on the normalisation of your choice, we either
3a. Run standard functions to select variable genes, and scale normalised gene counts. For downstream analysis it is beneficial to focus on genes that exhibit high cell-to-cell variation, that is they are highly expressed in some cells and lowly in others. To be able to compare normalised gene counts between genes, gene counts are further scaled to have zero mean and unit variance (z-score).
3b. Run SCTransform, a new and more sophisticated normalisation method that replaces the previous functions (normalisation, variable genes and scaling).

Note that removing all signal associated to cell cycle can negatively impact downstream analysis. For example, in differentiating processes, stem cells are quiescent and differentiated cells are proliferating (or vice versa), and removing all cell cycle effects can blur the distinction between these cells. As an alternative, we can remove the difference between G2M and S phase scores. This way, signals separating non-cycling and cycling cells will be maintained, while differences amongst proliferating cells will be removed. For a more detailed explanation, see the cell cycle vignette for Seurat (“Satija Lab” 2020). Cell cycle effects removed for this report: FALSE; all cell cycle effects removed for this report: FALSE.

While raw data is typically used for statistical tests such as finding marker genes, normalised data is mainly used for visualising gene expression values. Scaled data include variable genes only, potentially without cell cycle effects, and are mainly used to determine the structure of the dataset(s) with Principal Component Analysis, and indirectly to cluster and visualise cells in 2D space.

# Normalise data the original way
#   This is required to score cell cycle
#   https://github.com/satijalab/seurat/issues/1679
sc = purrr::map(sc, Seurat::NormalizeData, normalization.method = "LogNormalize", scale.factor=10000, verbose=FALSE)

# Determine cell cycle effect per sample 
sc = purrr::map(list_names(sc), function(n) {
  sc[[n]] = cc_scoring(sc=sc[[n]], genes_s=genes_s[,2], genes_g2m=genes_g2m[,2], name=n)
  if (all(is.na(sc[[n]][["S.Score"]])) & all(is.na(sc[[n]][["G2M.Score"]]))) param$cc_remove=FALSE
  return(sc[[n]])
})

# If cell cycle effects should be removed, we first score cells 
# The effect is then removed in the following chunk 
if (param$cc_remove) {
# Add to vars that need to regressed out during normalisation
  if (param$cc_remove_all) {
    # Remove all signal associated to cell cycle
    param$vars_to_regress = unique(c(param$vars_to_regress, "S.Score", "G2M.Score"))
    param$latent_vars = unique(c(param$latent_vars, "S.Score", "G2M.Score"))
  } else {
    # Don't remove the difference between cycling and non-cycling cells 
    param$vars_to_regress = unique(c(param$vars_to_regress, "CC.Difference"))
    param$latent_vars = unique(c(param$latent_vars, "CC.Difference"))
  }  
}

if (param$norm == "RNA") { 
  # Find variable features from normalised data (unaffected by scaling)
  sc = purrr::map(sc, Seurat::FindVariableFeatures, selection.method = "vst", nfeatures = 3000, verbose=FALSE)
  
  # Scale 
  # Note: For a single dataset where no integration/merging is needed, all features can already be scaled here. 
  #   Otherwise, scaling of all features will be done after integration/merging.
  if (param$integrate_samples[["method"]]=="single") {
    sc[[1]] = Seurat::ScaleData(sc[[1]], 
                      features=rownames(sc[[1]][["RNA"]]),
                      vars.to.regress=param$vars_to_regress, 
                      verbose=FALSE) 
  }
} else if (param$norm == "SCT") {
  # Run SCTransform
  #
  # This is a new normalisation method that replaces previous Seurat functions 'NormalizeData', 'FindVariableFeatures', and 'ScaleData'. 
  # vignette: https://satijalab.org/seurat/v3.0/sctransform_vignette.html
  # paper: https://www.biorxiv.org/content/10.1101/576827v2
  # Normalised data end up here: sc@assays$SCT@data
  # Note: For a single dataset where no integration is needed, all features can already be scaled here. 
  #   Otherwise, it is enough to scale only the variable features.
  # Note: It is not guaranteed that all genes are successfully normalised with SCTransform. 
  #   Consequently, some genes might be missing from the SCT assay. 
  #   See: https://github.com/ChristophH/sctransform/issues/27
  sc = purrr::map(list_names(sc), function(n) { 
    SCTransform(sc[[n]], 
                vars.to.regress=param$vars_to_regress, 
                min_cells=param$feature_filter[[n]][["min_cells"]], 
                verbose=FALSE, 
                return.only.var.genes=!(param$integrate_samples[["method"]]=="single")) 
  })
}

Variable genes

Experience shows that 1,000-2,000 genes with the highest cell-to-cell variation are often sufficient to describe the global structure of a single cell dataset. For example, cell type-specific genes typically highly vary between cells. Housekeeping genes, on the other hand, are similarly expressed across cells and can be disregarded to differentiate between cells.

To determine variable genes, we need to separate biological variability from technical variability. Technical variability arises especially for lowly expressed genes, where high variability corresponds to small absolute changes that we are not interested in. Here, we use the variance-stabilizing transformation (vst) method implemented in Seurat (Hafemeister and Satija (2019)). This method first models the technical variability as a relationship between mean gene expression and variance using local polynomial regression. The model is then used to calculate the expected variance based on the observed mean gene expression. The difference between the observed and expected variance is called residual variance and likely reflects biological variability. The top 3,000 variable genes are used for further analysis.

fig_height_vf = 5 * ceiling(length(names(sc))/2)

p_list = purrr::map(list_names(sc), function(n) {
  top10 = head(Seurat::VariableFeatures(sc[[n]], assay=param$norm), 10)
  p = Seurat::VariableFeaturePlot(sc[[n]], 
                                  assay=param$norm, 
                                  selection.method=ifelse(param$norm=="RNA", "vst", "sct"), 
                                  col=c("grey", param$col)) + 
    AddStyle(title=n) + 
    theme(legend.position=c(0.2, 0.8), legend.background=element_rect(fill=alpha("white", 0.0)))
  p = LabelPoints(plot=p, points=top10, repel=TRUE, xnudge=0, ynudge=0)
  return(p)
})

p = patchwork::wrap_plots(p_list, ncol=2) + patchwork::plot_annotation("Variable genes")
p

Combining multiple samples

if (param$integrate_samples[["method"]]!="single") {
  
  # When merging, feature meta-data is removed by Seurat entirely; save separately for each assay and add again afterwards
  assay_names = unique(purrr::flatten_chr(purrr::map(list_names(sc), function(n) { Seurat::Assays(sc[[n]]) } )))
  
  # Loop through all assays and accumulate meta data
  sc_feature_metadata = purrr::map(values_to_names(assay_names), function(a) {
    # "feature_id", "feature_name", "feature_type" are accumulated for all assays and stored just once
    # This step is skipped for assays that do not contain all three types of feature information
    contains_neccessary_columns = purrr::map_lgl(list_names(sc), function(n) { 
      all(c("feature_id", "feature_name", "feature_type") %in% colnames(sc[[n]][[a]][[]])) 
      })

    if (all(contains_neccessary_columns)) {
      feature_id_name_type = purrr::map(sc, function(s) return(s[[a]][[c("feature_id", "feature_name", "feature_type")]]) )
      feature_id_name_type = purrr::reduce(feature_id_name_type, function(df_x, df_y) {
        new_rows = which(!rownames(df_y) %in% rownames(df_x))
        if (length(new_rows) > 0) return(rbind(df_x, df_y[new_rows, ]))
        else return(df_x)
      })
      feature_id_name_type$row_names = rownames(feature_id_name_type)
    } else {
      feature_id_name_type = NULL
    }
    
    # For all other meta-data, we prefix column names with the dataset
    other_feature_data = purrr::map(list_names(sc), function(n) {
      df = sc[[n]][[a]][[]]
      if (contains_neccessary_columns[[n]]) df = df %>% dplyr::select(-dplyr::one_of(c("feature_id", "feature_name", "feature_type"), c()))
      if (ncol(df) > 0) colnames(df) = paste(n, colnames(df), sep=".")
      df$row_names = rownames(df)
      return(df)
    })
    
    # Now join everything by row_names by full outer join
    if (!is.null(feature_id_name_type)) {
      feature_data = purrr::reduce(c(list(feature_id_name_type=feature_id_name_type), other_feature_data), dplyr::full_join, by="row_names")
    } else {
      feature_data = purrr::reduce(other_feature_data, dplyr::full_join, by="row_names")
    }
    rownames(feature_data) = feature_data$row_names
    feature_data$row_names = NULL
    
    return(feature_data)
  })
  
  # When merging, cell metadata are merged but factors are not kept
  sc_cell_metadata = suppressWarnings(purrr::map_dfr(sc, function(s){ s[[]] }) %>% as.data.frame())
  sc_cell_metadata_factor_levels = purrr::map(which(sapply(sc_cell_metadata, is.factor)), function(n) {
    return(levels(sc_cell_metadata[, n, drop=TRUE]))
  })
}

# Standard method for integrating multiple samples. 
#   Best performance but computationally intensive.
if (param$integrate_samples[["method"]]=="standard") {
  # Note "Assay names should only have numbers and letters: Warnung: Keys should be one or more alphanumeric characters followed by an underscore, setting key from rna_integrated_ to rnaintegrated_" (seurat/R/object.R)
  
  # The integration step will temporarily occupy a lot of memory. 
  #   However, R has problems with freeing unused memory.
  #   By wrapping the steps into a function, hopefully this works a bit better.
  run_standard_integration = function(sc_objs, ndims=30, vars_to_regress=c(), feature_filter=c(), verbose=FALSE, assay="RNA") {
    # How many neighbors to use when filtering anchors; also adjust weight
    k.filter = min(200, min(sapply(sc_objs, ncol)))
    k.weight = min(100, k.filter)

    
    # Find integration anchors for assay RNA
    if (assay == "RNA") {
      integrate_RNA_anchors = Seurat::FindIntegrationAnchors(object.list=sc_objs, 
                                                             dims=1:ndims, 
                                                             anchor.features=3000, 
                                                             k.filter=k.filter,
                                                             verbose=verbose)
      sc_objs = Seurat::IntegrateData(integrate_RNA_anchors, 
                                      new.assay.name="RNAintegrated",
                                      dims=1:ndims,
                                      k.weight=k.weight,
                                      verbose=verbose)
      # According to Seurat, we need to scale data again for "RNAintegrated", and "RNA"
      sc_objs = Seurat::ScaleData(sc_objs, 
                                  features=rownames(sc_objs[["RNAintegrated"]]), 
                                  vars.to.regress=vars_to_regress, 
                                  assay="RNAintegrated",
                                  verbose=verbose)
      DefaultAssay(sc_objs) = "RNA"
      sc_objs = Seurat::ScaleData(sc_objs, 
                                  features=rownames(sc_objs[["RNA"]]), 
                                  vars.to.regress=vars_to_regress, 
                                  assay="RNA",
                                  verbose=verbose)
      rm(integrate_RNA_anchors)

    } else if (assay == "SCT") {
      # Find integration anchors for assay SCT
      integrate_SCT_features = SelectIntegrationFeatures(object.list=sc_objs, 
                                                         nfeatures=3000,
                                                         verbose=verbose)
      sc_objs = PrepSCTIntegration(object.list=sc_objs, 
                                   anchor.features=integrate_SCT_features, 
                                   assay=rep("SCT",length(sc_objs)),
                                   verbose=verbose)
      integrate_SCT_anchors = FindIntegrationAnchors(object.list=sc_objs,
                                                     dims=1:ndims, 
                                                     normalization.method="SCT", 
                                                     anchor.features=integrate_SCT_features, 
                                                     k.filter=k.filter,
                                                     verbose=verbose)
      sc_objs = Seurat::IntegrateData(integrate_SCT_anchors, 
                                      new.assay.name="SCTintegrated",
                                      normalization.method="SCT",
                                      dims=1:ndims, 
                                      k.weight=k.weight,
                                      verbose=verbose)
      # We need to re-run SCTransform for the "SCT" assay again, to normalise on the complete dataset
      DefaultAssay(sc_objs) = "SCT"
      min_cells_overall = max(purrr::map_int(feature_filter, function(f) as.integer(f[["min_cells"]])))
      sc_objs = SCTransform(sc_objs, 
                            vars.to.regress=vars_to_regress, 
                            min_cells=min_cells_overall, 
                            verbose=FALSE, 
                            return.only.var.genes=FALSE)
      rm(integrate_SCT_features, integrate_SCT_anchors)
    }
    
    # Call garbage collector to free memory (hope it helps)
    gc(verbose=verbose)
    return(sc_objs)
  }
  
  # call function
  sc = run_standard_integration(sc, ndims=param$integrate_samples[["dimensions"]], vars_to_regress=param$vars_to_regress, feature_filter=param$feature_filter, assay=param$norm)
  
  # Add feature metadata
  for (a in Seurat::Assays(sc)) {
    if (a %in% names(sc_feature_metadata)) {
      sc[[a]] = Seurat::AddMetaData(sc[[a]], sc_feature_metadata[[a]][rownames(sc[[a]]),, drop=FALSE])
    }
  }
  
  # Fix cell metadata factors
  for (f in names(sc_cell_metadata_factor_levels)) {
    sc[[f]] = factor(sc[[f, drop=TRUE]], levels=sc_cell_metadata_factor_levels[[f]])
  }
  
  # Set default assay (will be the integrated version)
  DefaultAssay(sc) = paste0(param$norm, "integrated")  
  
  message("Data values for all samples have been integrated.")
  print(sc)
}

× (Message)
Data values for all samples have been integrated.

## An object of class Seurat 
## 24312 features across 3918 samples within 2 assays 
## Active assay: RNAintegrated (3000 features, 3000 variable features)
##  1 other assay present: RNA

Relative log expression

n_cells_rle_plot = min(100, sc[["orig.ident"]] %>% table() %>% min())

# Sample at most 100 cells per dataset and save their identity
cells_RLE_subset = sc[["orig.ident"]] %>% tibble::rownames_to_column() %>% 
  dplyr::group_by(orig.ident) %>% 
  dplyr::sample_n(size=n_cells_rle_plot) %>% 
  dplyr::select(rowname, orig.ident)
  
cells_RLE_sample = cells_RLE_subset %>% dplyr::pull(orig.ident)
cells_RLE_subset = cells_RLE_subset %>% dplyr::pull(rowname)

To better understand the efficiency of the applied normalisation procedures, we plot the relative log expression of genes in at most 100 randomly selected cells per sample before and after normalisation. This type of plot reveals unwanted variation in your data. The concept is taken from Gandolfo and Speed (2018). In brief, we remove variation between genes, leaving only variation between samples. If expression levels of most genes are similar in all cell types, sample heterogeneity is a sign of unwanted variation.

For each gene, we calculate its median expression across all cells, and then calculate the deviation from this median for each cell. For each cell, we plot the median expression (black), the interquartile range (lightgrey), whiskers defined as 1.5 times the interquartile range (darkgrey), and outliers (#374E55FF, #DF8F44FF)

Raw counts

# Get counts from assay RNA
data_RLE_raw = GetAssayData(subset(sc, cells=cells_RLE_subset), assay="RNA", slot="counts")

# Plot
p = PlotRLE(as.matrix(log2(data_RLE_raw + 1)), id=cells_RLE_sample, col=param$col_samples) + labs(title="log2(raw counts + 1)")
p

Normalised data

Dependent on the context, this tab refers to different data:

• Single sample: RNA normalisation of the single sample
  
• Multiple samples that were merged: Combined RNA normalisation post merging of all samples
  
• Multiple samples that were integrated: Separate RNA normalisation prior to integration of all samples

# Get normalised data before integration
data_RLE_norm = GetAssayData(subset(sc, cells=cells_RLE_subset), assay=param$norm, slot="data")

# Plot
p = PlotRLE(as.matrix(data_RLE_norm), id=cells_RLE_sample, col=param$col_samples) + labs(title="Normalised data")
p 

Integrated data

if (! param$integrate_samples[["method"]] %in% c("single", "merge")) {
  # Get normalised data for integrated assays
  data_RLE_int = GetAssayData(subset(sc, cells=cells_RLE_subset), assay=paste0(param$norm, "integrated"), slot="data")

  # Plot
  p = PlotRLE(as.matrix(data_RLE_int), id=cells_RLE_sample, col=param$col_samples) + labs(title="Normalised integrated data")
  p
} else {
  message("No integrated data available.")
}

Dimensionality reduction

A single-cell dataset of 20,000 genes and 5,000 cells has 20,000 dimensions. At this point of the analysis, we have already reduced the dimensionality of the dataset to 3,000 variable genes. The biological manifold however can be described by far fewer dimensions than the number of (variable) genes. Dimension reduction methods aim to find these dimensions. There are two general purposes for dimension reduction methods: to summarise a dataset, and to visualise a dataset.

We use Principal Component Analysis (PCA) to summarise a dataset, overcoming noise and reducing the data to its essential components. Each principal component (PC) represents a “metafeature” that combines information across a correlated gene set. Later, we use Uniform Manifold Approximation and Projection (UMAP) to visualise the dataset, placing similar cells together in 2D space, see below.

To decide how many PCs to include in downstream analyses, we visualise the cells and genes that define the PCA.

# Run PCA for default normalisation
sc = Seurat::RunPCA(sc, features=Seurat::VariableFeatures(object=sc), verbose=FALSE)

p_list = Seurat::VizDimLoadings(sc, dims=1:2, reduction="pca", col=param$col, combine=FALSE)
for (i in seq(p_list)) p_list[[i]] = p_list[[i]] + AddStyle()
p =  patchwork::wrap_plots(p_list, ncol = 2) + patchwork::plot_annotation("Top gene loadings of the first two PCs") 
p

p = Seurat::DimPlot(sc, reduction="pca", cols=param$col_samples) + 
  AddStyle(title="Cells arranged by the first two PCs", legend_position="bottom")
p

p = Seurat::DimHeatmap(sc, dims=1:1, cells=min(500, ncol(sc)), balanced=TRUE, fast=FALSE)
p

p = Seurat::DimHeatmap(sc, dims=1:20, cells=min(500, ncol(sc)), balanced=TRUE, fast=FALSE)
p

Dimensionality of the dataset

We next need to decide how many PCs we want to use for our analyses. The following “Elbow plot” is designed to help us make an informed decision. PCs are ranked based on the percentage of variance they explain.

For the current dataset, 10 PCs were chosen.

# More approximate technique used to reduce computation time
p = Seurat::ElbowPlot(sc, ndims=20) + 
  geom_vline(xintercept=param$pc_n + .5, col="firebrick", lty=2) + 
  AddStyle(title="Elbow plot") 
p

Clustering

Seurat’s clustering method first constructs a graph structure, where nodes are cells and edges are drawn between cells with similar gene expression patterns. Technically speaking, Seurat first constructs a K-nearest neighbor (KNN) graph based on Euclidean distance in PCA space, and refines edge weights between cells based on the shared overlap in their local neighborhoods (Jaccard similarity). To partition the graph into highly interconnected parts, cells are iteratively grouped together using the Leiden algorithm.

# Note: I changed the seed in ./lib/python3.6/site-packages/leidenalg/functions.py to 11 for reproducibility
# The number of clusters can be optimized by tuning 'resolution' -> based on feedback from the client whether or not clusters make sense

# Choose the number of PCs to use for clustering
sc = Seurat::FindNeighbors(sc, dims=1:param$pc_n, verbose=FALSE)

# Cluster using the Leiden algorithm
# Paper to Leiden algorithm: https://www.nature.com/articles/s41598-019-41695-z
# Seurat vignette suggests resolution parameter between 0.4-1.2 for datasets of about 3k cells
# Taken from documentation:
#   Method for running leiden (defaults to matrix which is fast for small datasets). Enable method = "igraph" to avoid casting large data to a dense matrix.
# Note Katrin: 
#   This is implemented as default recently and will be updated with a new Seurat release
#   https://github.com/satijalab/seurat/pull/3233
sc = Seurat::FindClusters(sc, resolution=param$cluster_resolution, algorithm=4, verbose=FALSE, method="igraph")

# Construct phylogenetic tree relating the 'average' cell from each cluster
sc = BuildClusterTree(sc, dims=1:param$pc_n, verbose=FALSE)

# Set up colors for clusters
cluster_names = levels(sc$seurat_clusters)
param$col_clusters = GenerateColours(num_colours=length(cluster_names), palette=param$col_palette_clusters)
names(param$col_clusters) = cluster_names

Visualisation with UMAP

We use a UMAP to visualise and explore a dataset. The goal is to place similar cells together in 2D space, and learn about the biology underlying the data. Cells are color-coded according to the graph-based clustering, and clusters typcially co-localise on the UMAP.

Take care not to mis-read a UMAP:

• Parameters influence the plot (we use defaults here)
  
• Cluster sizes relative to each other mean nothing, since the method has a local notion of distance
  
• Distances between clusters might not mean anything
  
• You may need more than one plot

For a nice read to intuitively understand UMAP, see (“Understanding Umap” 2019).

Coloured by cluster

# Default UMAP
sc = suppressWarnings(Seurat::RunUMAP(sc, dims=1:param$pc_n, verbose=FALSE, umap.method="uwot"))

# 3D UMAP
sc = suppressWarnings(Seurat::RunUMAP(sc, dims=1:param$pc_n, n.components=3, reduction.name="umap3d", reduction.key="UMAP3D_", verbose=FALSE, umap.method="uwot"))

# Note that you can set `label = TRUE` or use the LabelClusters function to help label individual clusters
cluster_cells = table(sc@active.ident)
cluster_labels = paste0(levels(sc@active.ident)," (", cluster_cells[levels(sc@active.ident)],")")
p = Seurat::DimPlot(sc, reduction="umap", label=TRUE) + 
  AddStyle(title="UMAP, cells coloured by cluster identity", legend_position="bottom", legend_title="Clusters") + 
  scale_color_manual(values=param$col_clusters, labels=cluster_labels)
p

Coloured by sample

# Add a UMAP that is coloured by sample of origin
cell_samples = sc[[]] %>% dplyr::pull(orig.ident) %>% unique() %>% sort()

# Note: This is a hack to colour by sample but label by Cluster
p = Seurat::DimPlot(sc, group.by="orig.ident", pt.size=1, cols=param$col_samples) +
  AddStyle(title="UMAP, cells coloured by sample of origin", legend_position="bottom")
p$data$seurat_clusters = sc[["seurat_clusters"]][rownames(p$data), ]
p = LabelClusters(p, id="seurat_clusters")
p

Distribution of cells in clusters

# Count cells per cluster per sample 
cell_samples = sc[[]] %>% dplyr::pull(orig.ident) %>% unique() %>% sort()
cell_clusters = sc[[]] %>% dplyr::pull(seurat_clusters) %>% unique() %>% sort()
tbl = mapply(function(cl) { 
  sapply(cell_samples, function(s) sc[[]] %>% dplyr::filter(orig.ident==s, seurat_clusters==cl) %>% nrow())
  }, cell_clusters)
colnames(tbl) = paste0("Cl_", cell_clusters)
rownames(tbl) = paste0(cell_samples, "_n")

# Add percentages
tbl_perc = round(t(tbl) / colSums(tbl) * 100, 2) %>% t()
rownames(tbl_perc) = gsub(rownames(tbl_perc), pattern="_n$", replacement="_perc", perl=TRUE)
tbl = rbind(tbl, tbl_perc)

# Add enrichment
if (length(cell_samples) > 1) tbl = rbind(tbl, cells_fisher(sc))

# Sort
tbl = tbl[order(rownames(tbl)), ]

# Plot percentages
tbl_bar = tbl[paste0(cell_samples, "_perc"), ] %>% 
  tibble::rownames_to_column(var="Sample") %>%
  tidyr::pivot_longer(tidyr::starts_with("Cl"), names_to="Cluster", values_to="Percentage")
tbl_bar$Cluster = tbl_bar$Cluster %>% gsub(pattern="^Cl_", replacement="", perl=TRUE) %>% factor(levels=sc$seurat_clusters %>% levels())
tbl_bar$Sample = tbl_bar$Sample %>% gsub(pattern="_perc$", replacement="", perl=TRUE) %>% as.factor()
tbl_bar$Percentage = as.numeric(tbl_bar$Percentage)
p = ggplot(tbl_bar, aes(x=Cluster, y=Percentage, fill=Sample)) + 
  geom_bar(stat="identity" ) +
  AddStyle(title="Percentage cells of samples in clusters",
           fill=param$col_samples,
           legend_title="Sample",
           legend_position="bottom")
p

The following table shows the number of cells per sample per cluster:

• n: Number of cells per sample per cluster
  
• perc: Percentage of cells per sample per cluster compared to all other cells of that cluster

In case the dataset contains 2 or more samples, we also calculate whether or not the number of cells of a sample in a cluster is significantly higher or lower than expected:

• oddsRatio: Odds ratio calculated for cluster c1 and sample s1 as (# cells s1 in c1 / # cells not s1 in c1) / (# cells s1 not in c1 / # cells not s1 not in c1)
  
• p: P-value calculated with a Fisher test to test whether “n” is higher or lower than expected

# Print table
knitr::kable(tbl, align="l", caption="Number of cells per cluster per sample") %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>% 
  kableExtra::scroll_box(width="100%")

Number of cells per cluster per sample

	Cl_1	Cl_2	Cl_3	Cl_4	Cl_5	Cl_6	Cl_7	Cl_8	Cl_9	Cl_10	Cl_11
pbmc_10x_n	704	577	578	410	369	305	251	202	122	51	39
pbmc_10x_perc	91.43	92.32	94.44	94.47	89.35	90.5	93.31	93.09	93.85	76.12	88.64
pbmc_10x.oddsRatio	0.9	1.04	1.55	1.53	0.69	0.8	1.21	1.17	1.32	0.26	0.67
pbmc_10x.p	8.0e-01	4.4e-01	9.5e-03	2.8e-02	9.9e-01	8.9e-01	2.6e-01	3.4e-01	2.9e-01	1.0e+00	8.7e-01
pbmc_smartseq2_sample1_n	66	48	34	24	44	32	18	15	8	16	5
pbmc_smartseq2_sample1_perc	8.57	7.68	5.56	5.53	10.65	9.5	6.69	6.91	6.15	23.88	11.36
pbmc_smartseq2_sample1.oddsRatio	1.12	0.96	0.65	0.65	1.45	1.25	0.82	0.86	0.76	3.79	1.5
pbmc_smartseq2_sample1.p	2.5e-01	6.2e-01	9.9e-01	9.8e-01	2.1e-02	1.5e-01	8.1e-01	7.5e-01	8.2e-01	4.4e-05	2.7e-01

# Reset default assay, so we won't plot integrated data
# Note: We need integrated data for UMAP, clusters
DefaultAssay(sc) = param$norm

Cell Cycle Effect

How much do gene expression profiles in the dataset reflect the cell cycle phases the single cells were in? After initial normalisation, we determined the effects of cell cycle heterogeneity by calculating a score for each cell based on its expression of G2M and S phase markers. Scoring is based on the strategy described in Tirosh et al. (2016), and human gene symbols are translated to gene symbols of the species of interest using biomaRt. This section of the report visualises the above calculated cell cycle scores.

# Get a feeling for how many cells are affected
p1 = ggplot(sc[[]], aes(x=S.Score, y=G2M.Score, colour=Phase)) + 
  geom_point() + 
  scale_x_continuous("G1/S score") + 
  scale_y_continuous("G2/M score") + 
  AddStyle()
p2 = ggplot(sc@meta.data %>% 
              dplyr::group_by(seurat_clusters,Phase) %>% 
              dplyr::summarise(num_reads=length(Phase)), 
            aes(x=seurat_clusters, y=num_reads, fill=Phase)) + 
  geom_bar(stat="identity", position="fill") + 
  scale_x_discrete("Seurat clusters") + 
  scale_y_continuous("Fraction of cells") + 
  AddStyle()
p = p1 + p2 & theme(legend.position="bottom")
p = p + patchwork::plot_annotation(title="Cell cycle phases")
p

UMAP coloured by cell cycle phases

# UMAP with phases superimposed
# Note: This is a hack to colour by phase but label by Cluster
p = Seurat::DimPlot(sc, group.by="Phase", pt.size=1) + 
  AddStyle(title="UMAP, cells coloured by cell cycle phases", legend_title="Phase")
p$data$seurat_clusters = sc[["seurat_clusters"]][rownames(p$data), ]
p = LabelClusters(p, id="seurat_clusters")
p

UMAP coloured by S phase

p = Seurat::FeaturePlot(sc, features="S.Score", pt.size=1, min.cutoff="q1", max.cutoff="q99", label=TRUE, cols=c("lightgrey", param$col)) + 
  AddStyle(title="UMAP, cells coloured by S phase")
p

UMAP coloured by G2/M phase

p = Seurat::FeaturePlot(sc, features="G2M.Score", pt.size=1, min.cutoff="q1", max.cutoff="q99", cols=c("lightgrey", param$col), label=TRUE) + 
  AddStyle(title="UMAP, cells coloured by G2M phase")
p

UMAP coloured by the difference between S and G2/M phase

p = Seurat::FeaturePlot(sc, features="CC.Difference", pt.size=1, min.cutoff="q1", max.cutoff="q99", cols=c("lightgrey", param$col), label=TRUE) +
  AddStyle(title="UMAP, cells coloured by CC.Difference")
p

Cluster QC

Do cells in individual clusters have particularly high counts, detected genes or mitochondrial content?

Number of counts

p1 = suppressMessages(Seurat::FeaturePlot(sc, features="nCount_RNA", label=TRUE) + 
  AddStyle(title="Feature plot") + 
  scale_colour_gradient(low="lightgrey", high=param$col, trans="log10"))

p2 = ggplot(sc[[]], aes(x=seurat_clusters, y=nCount_RNA, fill=seurat_clusters)) + 
  geom_violin(scale="width") + 
  AddStyle(title="Violin plot (log10 scale)", fill=param$col_clusters,
           xlab="Cluster", legend_position="none") + 
  scale_y_log10()

p = p1 | p2 
p = p + patchwork::plot_annotation(title="Summed raw counts (nCount_RNA, log10 scale)")
p

Number of features

p1 = suppressMessages(Seurat::FeaturePlot(sc, features="nFeature_RNA", label=TRUE) + 
  AddStyle(title="Feature plot") + 
  scale_colour_gradient(low="lightgrey", high=param$col, trans="log10"))

p2 = ggplot(sc[[]], aes(x=seurat_clusters, y=nFeature_RNA, fill=seurat_clusters)) + 
  geom_violin(scale="width") + 
  AddStyle(title="Violin plot", fill=param$col_clusters,
           xlab="Cluster", legend_position="none") + 
  scale_y_log10()

p = p1 | p2 
p = p + patchwork::plot_annotation(title="Number of features with raw count > 0 (nFeature_RNA, log10 scale)")
p

Percent mitochondrial reads

p1 = Seurat::FeaturePlot(sc, features="percent_mt", cols=c("lightgrey", param$col), label=TRUE) + 
  AddStyle(title="Feature plot")

p2 = ggplot(sc[[]], aes(x=seurat_clusters, y=percent_mt, fill=seurat_clusters)) + 
  geom_violin(scale="width") + 
  AddStyle(title="Violin plot", fill=param$col_clusters,
           xlab="Cluster", legend_position="none")
p = p1 | p2 
p = p + patchwork::plot_annotation(title="Percent of mitochondrial features (percent_mt)")
p

Known marker genes

Do cells in individual clusters express provided known marker genes?

known_markers_list=c()

# Overwrite empty list of known markers 
if (!is.null(param$file_known_markers)) {
  # Read known marker genes and map to rownames
  known_markers = openxlsx::read.xlsx(param$file_known_markers)
  known_markers_list = lapply(colnames(known_markers), function(x) {
    y = ensembl_to_seurat_rowname[known_markers[,x]] %>% 
      na.exclude() %>% unique() %>% sort()
    m = !y %in% rownames(sc)
    if (any(m)){
      Warning(paste0("The following genes of marker list '", x, "' cannot be found in the data: ", first_n_elements_to_string(y[m], n=10)))
    }
    return(y[!m])
  })
  
  # Remove empty lists
  names(known_markers_list) = colnames(known_markers)
  is_empty = purrr::map_int(known_markers_list, .f=length) == 0 
  known_markers_list = known_markers_list[!is_empty]
}  

# Set plot options
if(length(known_markers_list) > 0) { 
  known_markers_n = length(known_markers_list) 
  known_markers_vect = unlist(known_markers_list) %>% unique() %>% sort()
  idx_dotplot = sapply(seq(known_markers_list), function(x) length(known_markers_list[[x]]) <= 50)
  idx_avgplot = sapply(seq(known_markers_list), function(x) length(known_markers_list[[x]]) >= 10)
} else { 
  known_markers_n=0
  idx_dotplot = idx_avgplot = FALSE
  known_markers_vect = c()
}

# Dotplots and average feature plots
# The height of 1 row (= 1 plot) is fixed to 5 
fig_height_knownMarkers_dotplot = max(5, 5 * sum(idx_dotplot))
fig_height_knownMarkers_avgplot = max(5, 5 * sum(idx_avgplot))

# Individual feature plots
# Each row contains 2 plots
# We fix the height of each plot to the same height as is used later for DEGs
height_per_row = max(2, 0.3 * length(unique(Idents(sc))))
nr_rows = ceiling(length(known_markers_vect)/2)
fig_height_knownMarkers_vect = max(5, height_per_row * nr_rows)

You provided 7 list(s) of known marker genes. In the following tabs, you find:

• Dot plots for all gene lists containing at most 50 genes
  
• Average feature plots for all gene lists containing at least 10 genes
  
• Individual feature plots for all genes if there are no more than 100 genes in total

Dot plot(s)

A dot plot visualises how gene expression changes across different clusters. The size of a dot encodes the percentage of cells in a cluster that expresses the gene, while the color encodes the scaled average expression across all cells within the cluster. Per gene, we group cells based on cluster identity, calculate average expression per cluster, subtract the mean of average expression values and divide by the standard deviation. The resulting scores describe how high or low a gene is expressed in a cluster compared to all other clusters.

if ((known_markers_n > 0) & any(idx_dotplot)) {
  known_markers_dotplot = known_markers_list[idx_dotplot]
  p_list = list()
  for (i in seq(known_markers_dotplot)) {
    g = known_markers_dotplot[[i]]
    g = g[length(g):1]
    p_list[[i]] = suppressMessages(Seurat::DotPlot(sc, features=g) + 
      scale_colour_gradient2(low="steelblue", mid="lightgrey", high="darkgoldenrod1") +
      AddStyle(title=paste("Known marker genes:", names(known_markers_dotplot)[i]), 
               ylab="Cluster") + 
      theme(axis.text.x = element_text(angle=90, hjust=1, vjust=.5)))
  }
  p = patchwork::wrap_plots(p_list, ncol=1)
  print(p)
} else if ((known_markers_n > 0) & !any(idx_dotplot)) {
  message("This tab is used for dot plots for up to 50 genes. All provided lists are longer than this, and hence dot plots are skipped.")
} else {
  message("No known marker genes were provided and hence dot plots are skipped.")
}

Average feature plot(s)

An average feature plot visualises the average gene expression of each gene list on a single-cell level, subtracted by the aggregated expression of control feature sets. The color of the plot encodes the calculated scores, whereat positive scores suggest that genes are expressed more highly than expected.

if ((known_markers_n > 0) & any(idx_avgplot)) {
  known_markers_avgplot = known_markers_list[idx_avgplot]
  sc = Seurat::AddModuleScore(sc, features=known_markers_avgplot, ctrl=10, name="known_markers")
  idx_replace_names = grep("^known_markers[0-9]+$", colnames(sc@meta.data), perl=TRUE)
  colnames(sc@meta.data)[idx_replace_names] = names(known_markers_avgplot)
  p_list = Seurat::FeaturePlot(sc, features=names(known_markers_avgplot), cols=c("lightgrey", param$col), combine=FALSE, label=TRUE)
  for (i in seq(known_markers_avgplot)) {
    p_list[[i]] = p_list[[i]] + AddStyle(title=paste("Known marker genes:", names(known_markers_avgplot)[i]))
  }
  p = patchwork::wrap_plots(p_list, ncol=1)
  print(p)
} else if ((known_markers_n > 0) & !any(idx_avgplot)) {
  message("This tab is used to plot an average for 10 or more genes. All provided lists are shorter than this, and hence average feature plots are skipped.")
} else {
  message("No known marker genes were provided and hence average feature plots are skipped.")
}

× (Message)
This tab is used to plot an average for 10 or more genes. All provided lists are shorter than this, and hence average feature plots are skipped.

Individual feature plots

An individual feature plot colours single cells on the UMAP according to their normalised gene expression.

if ((known_markers_n > 0) & length(known_markers_vect) <= 100) {
  p_list = Seurat::FeaturePlot(sc, features=known_markers_vect, cols=c("lightgrey", param$col), combine=FALSE, label=TRUE)
  for (i in seq(p_list)) p_list[[i]] = p_list[[i]] + AddStyle()
  p = patchwork::wrap_plots(p_list, ncol=2)
  print(p)
} else if (length(known_markers_vect) > 100) { 
  message("This tab is used to plot up to 100 known marker genes. Your provided list is longer than this, and hence individual feature plots are skipped.")
} else {
  message("No known marker genes were provided and hence individual feature plots are skipped.")
}

Marker genes

We next identify genes that are differentially expressed in one cluster compared to all other clusters, based on raw “RNA” data and the method “MAST”. Resulting p-values are adjusted using the Bonferroni method. However, note that the p-values are likely inflated, since both clusters and marker genes were determined based on the same gene expression data, and there ought to be gene expression differences by design. The names of differentially expressed genes per cluster, alongside statistical measures and additional gene annotation are written to file.

# Find DEGs for every cluster compared to all remaining cells, report positive (=markers) and negative ones
# min.pct = requires feature to be detected at this minimum percentage in either of the two groups of cells 
# logfc.threshold = requires a feature to be differentially expressed on average by some amount between the two groups
# only.pos = find only positive markers 

# Review recommends using "MAST"; Mathias uses "LR"
# ALWAYS USE: assay="RNA" or assay="SCT"
# DONT USE: assay=integrated datasets; this data is normalised and contains only 2k genes
# Note: By default, the function uses slot="data". Mast requires log data, so this is the correct way to do it.
#   https://www.bioconductor.org/packages/release/bioc/vignettes/MAST/inst/doc/MAST-interoperability.html
markers = suppressMessages(Seurat::FindAllMarkers(sc, assay="RNA", test.use="MAST",
                                               only.pos=FALSE, min.pct=param$marker_pct, logfc.threshold=param$marker_log2FC,
                                               latent.vars=param$latent_vars, verbose=FALSE, silent=TRUE))

# If no markers were found, initialise the degs table so that further downstream (export) chunks run
if (ncol(markers)==0) markers = DegsEmptyMarkerResultsTable(levels(sc$seurat_clusters))

# For Seurat versions until 3.2, log fold change is based on the natural log. Convert to log base 2.
if ("avg_logFC" %in% colnames(markers) & !"avg_log2FC" %in% colnames(markers)) {
  lfc_idx = grep("avg_log\\S*FC", colnames(markers))
  markers[,lfc_idx] = marker_deg_results[,lfc_idx] / log(2)
  col_nms = colnames(markers)
  col_nms[2] = "avg_log2FC"
  colnames(markers) = col_nms
}

# Sort markers
markers = markers %>% DegsSort(group=c("cluster"))
  
# Filter markers 
markers_filt = DegsFilter(markers, cut_log2FC=param$marker_log2FC, cut_padj=param$marker_padj)
markers_found = nrow(markers_filt$all)>0

# Add average data to table
markers_out = cbind(markers_filt$all, DegsAvgDataPerIdentity(sc, genes=markers_filt$all$gene))

# Split by cluster and write to file
additional_readme = data.frame(Column=c("cluster",
                                        "p_val_adj_score",
                                        "avg_<assay>_<slot>_id<cluster>"), 
                               Description=c("Cluster",
                                             "Score calculated as follows: -log10(p_val_adj)*sign(avg_log2FC)",
                                             "Average expression value for cluster; <assay>: RNA or SCT; <slot>: raw counts or normalised data"))

markers_results_file = DegsWriteToFile(split(markers_out, markers_out$cluster),
                                       annot_ensembl=annot_ensembl,
                                       gene_to_ensembl=seurat_rowname_to_ensembl,
                                       additional_readme=additional_readme,
                                       file=paste0(param$path_out, "/markers_cluster_vs_rest.xlsx"))


# Plot number of differentially expressed genes
p = DegsPlotNumbers(markers_filt$all, 
                      group="cluster", 
                      title=paste0("Number of DEGs, comparing each cluster to the rest\n(FC=", 2^param$marker_log2FC, ", adj. p-value=", param$marker_padj, ")")) 


if (markers_found) {
  print(p)
} else {
  warning("No differentially expressed genes (cluster vs rest) found. The following related code is not executed, no related plots and tables are generated.")
}

Table of top marker genes

We use the term “marker genes” to specifically describe genes that are up-regulated in cells of one cluster compared to the rest.

if (markers_found) {
  markers_top = DegsUpDisplayTop(markers_filt$up, n=5)

  # Show table
  knitr::kable(markers_top, align="l", caption="Up to top 5 marker genes per cell cluster") %>% 
    kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>% 
    kableExtra::scroll_box(width="100%", height="700px") 
}

Up to top 5 marker genes per cell cluster

cluster	gene	avg_log2FC	p_val	p_val_adj	pct.1	pct.2
1	GZMH	2.150	0.0e+00	0.0e+00	0.986	0.264
1	NKG7	1.842	1.2e-316	2.6e-312	1.000	0.513
1	FGFBP2	1.935	1.8e-282	3.8e-278	0.908	0.241
1	CCL5.1	1.597	6.0e-280	1.3e-275	1.000	0.489
1	GZMB	1.395	5.4e-264	1.1e-259	0.887	0.221
2	DUSP2	1.221	5.4e-91	1.2e-86	0.773	0.422
2	GZMK	1.627	2.2e-69	4.6e-65	0.379	0.093
2	CD8A	1.010	6.2e-62	1.3e-57	0.682	0.336
2	CMC1	1.084	2.0e-39	4.2e-35	0.322	0.122
3	IL7R	1.768	1.8e-244	3.9e-240	0.946	0.292
3	LTB	1.850	1.5e-235	3.2e-231	0.938	0.337
3	LDHB	1.173	2.3e-150	4.8e-146	0.943	0.623
3	ZFP36L2	1.304	3.0e-146	6.3e-142	0.989	0.833
3	LEPROTL1	1.134	5.5e-133	1.2e-128	0.923	0.534
4	CD79A	3.997	0.0e+00	0.0e+00	1.000	0.018
4	HLA-DQA1	3.049	0.0e+00	0.0e+00	0.979	0.029
4	MS4A1	2.844	0.0e+00	0.0e+00	0.878	0.016
4	LINC00926	2.627	0.0e+00	0.0e+00	0.871	0.019
4	BANK1	2.392	0.0e+00	0.0e+00	0.836	0.011
5	S100A9	6.066	0.0e+00	0.0e+00	0.998	0.062
5	VCAN	4.367	0.0e+00	0.0e+00	0.988	0.026
5	FCN1	3.983	0.0e+00	0.0e+00	0.993	0.053
5	CD14	3.465	0.0e+00	0.0e+00	0.952	0.009
5	CLEC7A	2.696	0.0e+00	0.0e+00	0.959	0.056
6	KLRF1	2.245	3.0e-249	6.4e-245	0.807	0.044
6	GNLY	2.917	2.0e-210	4.3e-206	0.976	0.315
6	CD247	1.937	1.1e-205	2.4e-201	0.967	0.457
6	PRF1	2.351	6.1e-192	1.3e-187	0.973	0.363
6	GZMB	2.231	2.6e-191	5.5e-187	0.953	0.296
7	LEF1	1.709	5.3e-151	1.1e-146	0.758	0.078
7	CCR7	1.749	2.4e-139	5.2e-135	0.770	0.095
7	TCF7	1.382	6.1e-95	1.3e-90	0.859	0.314
7	IL7R	1.096	1.6e-88	3.3e-84	0.948	0.354
7	TRABD2A	1.231	4.6e-84	9.8e-80	0.561	0.077
8	KLRB1	2.661	7.8e-131	1.7e-126	0.871	0.188
8	GZMK	1.832	9.2e-113	2.0e-108	0.788	0.101
8	TNFAIP3	1.615	8.3e-77	1.8e-72	0.954	0.585
8	DUSP2	1.522	2.7e-68	5.7e-64	0.935	0.451
8	AQP3	1.862	2.4e-63	5.1e-59	0.507	0.104
9	LST1	3.941	7.6e-245	1.6e-240	1.000	0.198
9	IFITM3	3.400	1.2e-208	2.5e-204	0.992	0.268
9	FCGR3A	3.363	5.1e-200	1.1e-195	1.000	0.152
9	AIF1	3.580	1.5e-186	3.2e-182	1.000	0.166
9	CDKN1C.1	3.276	6.2e-185	1.3e-180	0.923	0.020
10	RGS10	4.206	5.3e-275	1.1e-270	0.776	0.407
10	MAX	4.290	2.3e-253	4.9e-249	0.851	0.292
10	TAGLN2	4.199	2.0e-249	4.2e-245	0.940	0.709
10	OST4	3.347	6.8e-210	1.4e-205	0.866	0.791
10	TUBA4A	4.447	7.2e-205	1.5e-200	0.851	0.402
11	HLA-DPB1.2	3.436	6.9e-82	1.5e-77	1.000	0.570
11	HLA-DPA1.2	3.639	1.6e-80	3.4e-76	1.000	0.550
11	FCER1A	2.980	2.4e-59	5.1e-55	0.773	0.009
11	CD74	3.186	2.3e-53	5.0e-49	1.000	0.775
11	PLD4	2.314	4.6e-52	9.8e-48	0.909	0.042

Visualisation of top marker genes

The following plots are exemplary to how we can visualise differentially expressed genes using the Seurat R-package. The selected genes are the top marker genes for each cluster, respectively.

if (markers_found) {
  # Get top 1 gene per cluster and plot
  if (nrow(markers_filt$all) > 0) {
    markers_examples = markers_filt$up %>% 
      dplyr::group_by(cluster) %>% 
      dplyr::top_n(n=1, wt=avg_log2FC) %>% 
      dplyr::select(cluster, gene) %>% 
      as.data.frame()
  } else { 
    markers_examples = data.frame(cluster=numeric(), gene=character())
  }
  markers_examples = setNames(markers_examples$gene, paste0(markers_examples$cluster, ": ", markers_examples$gene))
}

# Note: We need to run this chunk as it specifies a variable that is used in chunk definitions below
if (markers_found) {
  # Each row contains 2 plots
  nr_rows = ceiling(length(markers_examples)/2)

  # The height of each plot might depend on the number of clusters 
  height_per_row = max(2, 0.3 * length(unique(Idents(sc))))

  # Total height of plots 
  fig_height_markers = max(5, height_per_row * nr_rows)
} else {
  fig_height_markers = 7
}

Feature plots

if (markers_found) {
  # Shows gene expression on the UMAP
  p_list = Seurat::FeaturePlot(sc, features=markers_examples, cols=c("lightgrey", param$col), combine=FALSE, label=TRUE)
  for (i in seq(p_list)) p_list[[i]] = p_list[[i]] + AddStyle(title=names(markers_examples)[i])
  p = patchwork::wrap_plots(p_list, ncol=2) + 
    patchwork::plot_annotation(title="UMAP, cells coloured by normalised gene expression data, top 1 marker gene per cluster")
  p
}

Violin plots (raw)

if (markers_found) {
  # Violin plot of raw gene expression counts
  p_list = Seurat::VlnPlot(sc, features=markers_examples, assay="RNA", slot="counts", combine=FALSE, pt.size=0.2)
  for (i in seq(p_list)) p_list[[i]] = p_list[[i]] + AddStyle(title=names(markers_examples)[i], 
                                                              legend_title="Cluster", 
                                                              fill=param$col_clusters, 
                                                              xlab="Cluster")
  p = patchwork::wrap_plots(p_list, ncol=2) + 
    patchwork::plot_annotation(title="Violin plot of raw gene expression counts, top 1 marker gene per cluster") + 
    patchwork::plot_layout(guides = "collect") & theme(legend.position="bottom")
  suppressMessages(p)
}

Violin plots (normalised)

if (markers_found) {
  # Violin plot of normalised gene expression data
  p_list = Seurat::VlnPlot(sc, features=markers_examples, combine=FALSE, pt.size=0.2)
  for (i in seq(p_list)) p_list[[i]] = p_list[[i]] + AddStyle(title=names(markers_examples)[i], 
                                                              legend_title="Cluster", 
                                                              fill=param$col_clusters, 
                                                              xlab="Cluster") 
  p = patchwork::wrap_plots(p_list, ncol=2) + 
    patchwork::plot_annotation(title="Violin plot of normalised gene expression data, top 1 marker gene per cluster") +
    patchwork::plot_layout(guides = "collect") &
    theme(legend.position="bottom")
  suppressMessages(p)
}

Ridge plots (raw)

if (markers_found) {
  # Ridge plot of raw gene expression counts
  p_list = Seurat::RidgePlot(sc, features=markers_examples, assay="RNA", slot="counts", combine=FALSE)
  for (i in seq(p_list)) {
    p_list[[i]] = p_list[[i]] + AddStyle(title=names(markers_examples)[i], 
                                         legend_title="Cluster", 
                                         fill=param$col_clusters, 
                                         ylab="Cluster")
  }
  p = patchwork::wrap_plots(p_list, ncol=2) + 
    patchwork::plot_annotation(title="Ridge plot of raw gene expression counts, top 1 marker gene per cluster") + 
    patchwork::plot_layout(guides = "collect") &
    theme(legend.position="bottom")
  suppressMessages(p)
}

Ridge plots (normalised)

if (markers_found) {
  # Ridge plot of normalised gene expression data
  p_list = Seurat::RidgePlot(sc, features=markers_examples, slot="data", combine=FALSE)
  for (i in seq(p_list)) {
    p_list[[i]] = p_list[[i]] + AddStyle(title=names(markers_examples)[i], 
                                         legend_title="Cluster", 
                                         fill=param$col_clusters, 
                                         ylab="Cluster")
  }
  p = patchwork::wrap_plots(p_list, ncol=2) + 
    patchwork::plot_annotation(title="Ridge plot of normalised and log-transformed gene expression data, top 1 marker gene per cluster") + 
    patchwork::plot_layout(guides = "collect") &
    theme(legend.position="bottom")
  suppressMessages(p)
}

Dot plot

if (markers_found) {
  # Visualises how feature expression changes across different clusters
  p_list = lapply(levels(sc$seurat_clusters), function(cl) {
    genes = markers_top %>% dplyr::filter(cluster==cl) %>% dplyr::pull(gene)
    if (length(genes) > 0) { 
      genes = genes[length(genes):1]
      p = suppressMessages(Seurat::DotPlot(sc, features=genes) + 
        scale_colour_gradient2(low="steelblue", mid="lightgrey", high="darkgoldenrod1") +
        AddStyle(title=paste0("Top markers (up-regulated genes) for cluster ", cl), ylab="Cluster", legend_position="bottom") + 
        theme(axis.text.x = element_text(angle=90, hjust=1, vjust=.5)) + 
        guides(size=guide_legend(order=1)))
      return(p)
    } else { 
      message("No up-regulated genes for cluster ", cl, ".")
      return(NULL)
    }
  })
  
  p = patchwork::wrap_plots(p_list, ncol=2)
  print(p)
}

Expression per cluster per sample

If the dataset contains multiple samples, we can visualise the expression of a gene that is up-regulated in a cluster separately for each sample. For each cluster, we extract up-regulated genes, and visualise expression of these genes in all cells in that cluster, split by their sample of origin.

fig_height_degs_per_cl = max(5, 
                             max(2, 0.3 * (sc$orig.ident %>% unique() %>% length())) * length(levels(sc$seurat_clusters)) * 2)

Scaled dotplots

First, we plot scaled expression as explained above (see section Known marker genes). This plot allows us to judge whether the expression of a gene is increased in one sample as compared to the other samples.

p_list = list()
markers_filt_up_top = DegsUpDisplayTop(degs=markers_filt$up, n=50)
for (cl in levels(sc$seurat_clusters)) {  
  markers_filt_up_cl_top = markers_filt_up_top %>% 
    dplyr::filter(cluster==cl) %>% 
    dplyr::pull(gene)

  if (length(markers_filt_up_cl_top) > 0) {
    p_list[[cl]] = suppressMessages(Seurat::DotPlot(sc, features=markers_filt_up_cl_top, idents=cl, group.by="orig.ident") +
      scale_colour_gradient2(low="steelblue", mid="lightgrey", high="darkgoldenrod1") + 
      AddStyle(title=paste0("Up to 50 markers (up-regulated genes) for cluster ", cl), ylab="Cluster", legend_position="bottom") + 
      theme(axis.text.x = element_text(angle=90, hjust=1, vjust=.5)) + 
      guides(size=guide_legend(order=1)))
  }
}
p = patchwork::wrap_plots(p_list, ncol=1) + patchwork::plot_annotation("Dotplot per cluster") 
p

Non-scaled dotplots

Second, we plot normalised expression with no further scaling. This plot helps to get an impression of the total expression of a gene.

n_genes_max_dotplot = 50
p_list = list()
for (cl in levels(sc$seurat_clusters)) {            
  markers_filt_up_cl_top = markers_filt$up %>% 
    dplyr::filter(cluster==cl) %>% 
    dplyr::top_n(n=n_genes_max_dotplot, wt=p_val_adj_score) %>% 
    dplyr::pull(gene)
  if (length(markers_filt_up_cl_top) > 0) {
    p_list[[cl]] = Seurat::DotPlot(sc, features=markers_filt_up_cl_top, idents=cl, group.by="orig.ident", scale=FALSE, cols=c("lightgrey", param$col)) +
      AddStyle(title=paste0("Up to ", n_genes_max_dotplot, " markers (up-regulated genes) for cluster ", cl, " (not scaled)"), ylab="Cluster", legend_position="bottom") + 
      theme(axis.text.x = element_text(angle=90, hjust=1, vjust=.5)) + 
      guides(size=guide_legend(order=1))
  }
}
p = patchwork::wrap_plots(p_list, ncol=1) + patchwork::plot_annotation("Dotplot per cluster (not scaled)") 
p

Heatmaps

All up- and down-regulated genes

if (markers_found) {
  # This will sample 500 cells; the number of cells per seurat_cluster will be proportional
  cells_subset = ScSampleCells(sc, n=500, group="seurat_clusters", group_proportional=TRUE)
    
  # Heatmap of all differentially expressed genes
  p = Seurat::DoHeatmap(sc, features=markers_filt$all$gene, group.colors=param$col_clusters, label=FALSE, cells=cells_subset) + 
    NoLegend() + 
    theme(axis.text.y=element_blank()) +
    ggtitle("Heatmap of scaled gene expression data, all genes differentially expressed between a cluster and the rest")
  p
  
}

Top 300 up-regulated genes

if (markers_found) {
  # This will sample 500 cells; the number of cells per seurat_cluster will be proportional
  cells_subset = ScSampleCells(sc, n=500, group="seurat_clusters", group_proportional=TRUE)
  # With fig.height = 20, 300 features can be shown; distribute among clusters
  features_per_group = 300/length(levels(markers_filt$up$cluster))
  features_subset = markers_filt$up %>% 
    dplyr::group_by(cluster) %>% 
    dplyr::top_n(n=features_per_group, wt=avg_log2FC) %>% 
    dplyr::arrange(cluster, -avg_log2FC) %>%
    dplyr::pull(gene) %>%
    unique()
  
  # Heatmap of top up-regulated genes
  p = Seurat::DoHeatmap(sc, features=features_subset, group.colors=param$col_clusters, label=FALSE, cells=cells_subset) + 
    NoLegend() + 
    theme(axis.text.y=element_text(size=8)) +
    ggtitle("Heatmap of scaled gene expression data, top genes up-regulated in a cluster compared to the rest")
  p
}

Top 300 down-regulated genes

if (markers_found) {
  # This will sample 500 cells; the number of cells per seurat_cluster will be proportional
  cells_subset = ScSampleCells(sc, n=500, group="seurat_clusters", group_proportional=TRUE)
  # With fig.height = 20, 300 features can be shown; distribute among clusters
  features_per_group = 300/length(levels(markers_filt$down$cluster))
  features_subset = markers_filt$down %>% 
    dplyr::group_by(cluster) %>% 
    dplyr::top_n(n=features_per_group, wt=-avg_log2FC) %>% 
    dplyr::arrange(cluster, avg_log2FC) %>%
    dplyr::pull(gene) %>%
    unique()
  
  # Heatmap of top down-regulated genes
  p = Seurat::DoHeatmap(sc, features=features_subset, group.colors=param$col_clusters, label=FALSE, cells=cells_subset) + 
    NoLegend() + 
    theme(axis.text.y=element_text(size=8)) +
    ggtitle("Heatmap of scaled gene expression data, top genes up-regulated in a cluster compared to the rest")
  p
}

Functional enrichment analysis

To gain first insights into potential functions of cells in a cluster, we test for over-representation of functional terms amongst up- and down-regulated genes of each cluster. Over-represented terms are written to file.

We first translate gene symbols of up- and down-regulated genes per cluster into Entrez gene symbols, and then use the “enrichR” R-package to access the “Enrichr” website (Chen 2020). You can choose to test functional enrichment from a wide range of databases:

dbs_all = enrichR::listEnrichrDbs()
knitr::kable(dbs_all, align="l", caption="Enrichr databases") %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover")) %>% 
  kableExtra::scroll_box(width="100%", height="300px")

Enrichr databases

geneCoverage	genesPerTerm	libraryName	link	numTerms
13362	275	Genome_Browser_PWMs	http://hgdownload.cse.ucsc.edu/goldenPath/hg18/database/	615
27884	1284	TRANSFAC_and_JASPAR_PWMs	http://jaspar.genereg.net/html/DOWNLOAD/	326
6002	77	Transcription_Factor_PPIs		290
47172	1370	ChEA_2013	http://amp.pharm.mssm.edu/lib/cheadownload.jsp	353
47107	509	Drug_Perturbations_from_GEO_2014	http://www.ncbi.nlm.nih.gov/geo/	701
21493	3713	ENCODE_TF_ChIP-seq_2014	http://genome.ucsc.edu/ENCODE/downloads.html	498
1295	18	BioCarta_2013	https://cgap.nci.nih.gov/Pathways/BioCarta_Pathways	249
3185	73	Reactome_2013	http://www.reactome.org/download/index.html	78
2854	34	WikiPathways_2013	http://www.wikipathways.org/index.php/Download_Pathways	199
15057	300	Disease_Signatures_from_GEO_up_2014	http://www.ncbi.nlm.nih.gov/geo/	142
4128	48	KEGG_2013	http://www.kegg.jp/kegg/download/	200
34061	641	TF-LOF_Expression_from_GEO	http://www.ncbi.nlm.nih.gov/geo/	269
7504	155	TargetScan_microRNA	http://www.targetscan.org/cgi-bin/targetscan/data_download.cgi?db=vert_61	222
16399	247	PPI_Hub_Proteins	http://amp.pharm.mssm.edu/X2K	385
12753	57	GO_Molecular_Function_2015	http://www.geneontology.org/GO.downloads.annotations.shtml	1136
23726	127	GeneSigDB	http://genesigdb.org/genesigdb/downloadall.jsp	2139
32740	85	Chromosome_Location	http://software.broadinstitute.org/gsea/msigdb/index.jsp	386
13373	258	Human_Gene_Atlas	http://biogps.org/downloads/	84
19270	388	Mouse_Gene_Atlas	http://biogps.org/downloads/	96
13236	82	GO_Cellular_Component_2015	http://www.geneontology.org/GO.downloads.annotations.shtml	641
14264	58	GO_Biological_Process_2015	http://www.geneontology.org/GO.downloads.annotations.shtml	5192
3096	31	Human_Phenotype_Ontology	http://www.human-phenotype-ontology.org/	1779
22288	4368	Epigenomics_Roadmap_HM_ChIP-seq	http://www.roadmapepigenomics.org/	383
4533	37	KEA_2013	http://amp.pharm.mssm.edu/lib/keacommandline.jsp	474
10231	158	NURSA_Human_Endogenous_Complexome	https://www.nursa.org/nursa/index.jsf	1796
2741	5	CORUM	http://mips.helmholtz-muenchen.de/genre/proj/corum/	1658
5655	342	SILAC_Phosphoproteomics	http://amp.pharm.mssm.edu/lib/keacommandline.jsp	84
10406	715	MGI_Mammalian_Phenotype_Level_3	http://www.informatics.jax.org/	71
10493	200	MGI_Mammalian_Phenotype_Level_4	http://www.informatics.jax.org/	476
11251	100	Old_CMAP_up	http://www.broadinstitute.org/cmap/	6100
8695	100	Old_CMAP_down	http://www.broadinstitute.org/cmap/	6100
1759	25	OMIM_Disease	http://www.omim.org/downloads	90
2178	89	OMIM_Expanded	http://www.omim.org/downloads	187
851	15	VirusMINT	http://mint.bio.uniroma2.it/download.html	85
10061	106	MSigDB_Computational	http://www.broadinstitute.org/gsea/msigdb/collections.jsp	858
11250	166	MSigDB_Oncogenic_Signatures	http://www.broadinstitute.org/gsea/msigdb/collections.jsp	189
15406	300	Disease_Signatures_from_GEO_down_2014	http://www.ncbi.nlm.nih.gov/geo/	142
17711	300	Virus_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	323
17576	300	Virus_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	323
15797	176	Cancer_Cell_Line_Encyclopedia	https://portals.broadinstitute.org/ccle/home	967
12232	343	NCI-60_Cancer_Cell_Lines	http://biogps.org/downloads/	93
13572	301	Tissue_Protein_Expression_from_ProteomicsDB	https://www.proteomicsdb.org/	207
6454	301	Tissue_Protein_Expression_from_Human_Proteome_Map	http://www.humanproteomemap.org/index.php	30
3723	47	HMDB_Metabolites	http://www.hmdb.ca/downloads	3906
7588	35	Pfam_InterPro_Domains	ftp://ftp.ebi.ac.uk/pub/databases/interpro/	311
7682	78	GO_Biological_Process_2013	http://www.geneontology.org/GO.downloads.annotations.shtml	941
7324	172	GO_Cellular_Component_2013	http://www.geneontology.org/GO.downloads.annotations.shtml	205
8469	122	GO_Molecular_Function_2013	http://www.geneontology.org/GO.downloads.annotations.shtml	402
13121	305	Allen_Brain_Atlas_up	http://www.brain-map.org/	2192
26382	1811	ENCODE_TF_ChIP-seq_2015	http://genome.ucsc.edu/ENCODE/downloads.html	816
29065	2123	ENCODE_Histone_Modifications_2015	http://genome.ucsc.edu/ENCODE/downloads.html	412
280	9	Phosphatase_Substrates_from_DEPOD	http://www.koehn.embl.de/depod/	59
13877	304	Allen_Brain_Atlas_down	http://www.brain-map.org/	2192
15852	912	ENCODE_Histone_Modifications_2013	http://genome.ucsc.edu/ENCODE/downloads.html	109
4320	129	Achilles_fitness_increase	http://www.broadinstitute.org/achilles	216
4271	128	Achilles_fitness_decrease	http://www.broadinstitute.org/achilles	216
10496	201	MGI_Mammalian_Phenotype_2013	http://www.informatics.jax.org/	476
1678	21	BioCarta_2015	https://cgap.nci.nih.gov/Pathways/BioCarta_Pathways	239
756	12	HumanCyc_2015	http://humancyc.org/	125
3800	48	KEGG_2015	http://www.kegg.jp/kegg/download/	179
2541	39	NCI-Nature_2015	http://pid.nci.nih.gov/	209
1918	39	Panther_2015	http://www.pantherdb.org/	104
5863	51	WikiPathways_2015	http://www.wikipathways.org/index.php/Download_Pathways	404
6768	47	Reactome_2015	http://www.reactome.org/download/index.html	1389
25651	807	ESCAPE	http://www.maayanlab.net/ESCAPE/	315
19129	1594	HomoloGene	http://www.ncbi.nlm.nih.gov/homologene	12
23939	293	Disease_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	839
23561	307	Disease_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	839
23877	302	Drug_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	906
15886	9	Genes_Associated_with_NIH_Grants	https://grants.nih.gov/grants/oer.htm	32876
24350	299	Drug_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	906
3102	25	KEA_2015	http://amp.pharm.mssm.edu/Enrichr	428
31132	298	Gene_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	2460
30832	302	Gene_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	2460
48230	1429	ChEA_2015	http://amp.pharm.mssm.edu/Enrichr	395
5613	36	dbGaP	http://www.ncbi.nlm.nih.gov/gap	345
9559	73	LINCS_L1000_Chem_Pert_up	https://clue.io/	33132
9448	63	LINCS_L1000_Chem_Pert_down	https://clue.io/	33132
16725	1443	GTEx_Tissue_Sample_Gene_Expression_Profiles_down	http://www.gtexportal.org/	2918
19249	1443	GTEx_Tissue_Sample_Gene_Expression_Profiles_up	http://www.gtexportal.org/	2918
15090	282	Ligand_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	261
16129	292	Aging_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	286
15309	308	Aging_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	286
15103	318	Ligand_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	261
15022	290	MCF7_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	401
15676	310	MCF7_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	401
15854	279	Microbe_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	312
15015	321	Microbe_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	312
3788	159	LINCS_L1000_Ligand_Perturbations_down	https://clue.io/	96
3357	153	LINCS_L1000_Ligand_Perturbations_up	https://clue.io/	96
12668	300	L1000_Kinase_and_GPCR_Perturbations_down	https://clue.io/	3644
12638	300	L1000_Kinase_and_GPCR_Perturbations_up	https://clue.io/	3644
8973	64	Reactome_2016	http://www.reactome.org/download/index.html	1530
7010	87	KEGG_2016	http://www.kegg.jp/kegg/download/	293
5966	51	WikiPathways_2016	http://www.wikipathways.org/index.php/Download_Pathways	437
15562	887	ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X		104
17850	300	Kinase_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	285
17660	300	Kinase_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	285
1348	19	BioCarta_2016	http://cgap.nci.nih.gov/Pathways/BioCarta_Pathways	237
934	13	HumanCyc_2016	http://humancyc.org/	152
2541	39	NCI-Nature_2016	http://pid.nci.nih.gov/	209
2041	42	Panther_2016	http://www.pantherdb.org/pathway/	112
5209	300	DrugMatrix	https://ntp.niehs.nih.gov/drugmatrix/	7876
49238	1550	ChEA_2016	http://amp.pharm.mssm.edu/Enrichr	645
2243	19	huMAP	http://proteincomplexes.org/	995
19586	545	Jensen_TISSUES	http://tissues.jensenlab.org/	1842
22440	505	RNA-Seq_Disease_Gene_and_Drug_Signatures_from_GEO	http://www.ncbi.nlm.nih.gov/geo/	1302
8184	24	MGI_Mammalian_Phenotype_2017	http://www.informatics.jax.org/	5231
18329	161	Jensen_COMPARTMENTS	http://compartments.jensenlab.org/	2283
15755	28	Jensen_DISEASES	http://diseases.jensenlab.org/	1811
10271	22	BioPlex_2017	http://bioplex.hms.harvard.edu/	3915
10427	38	GO_Cellular_Component_2017	http://www.geneontology.org/	636
10601	25	GO_Molecular_Function_2017	http://www.geneontology.org/	972
13822	21	GO_Biological_Process_2017	http://www.geneontology.org/	3166
8002	143	GO_Cellular_Component_2017b	http://www.geneontology.org/	816
10089	45	GO_Molecular_Function_2017b	http://www.geneontology.org/	3271
13247	49	GO_Biological_Process_2017b	http://www.geneontology.org/	10125
21809	2316	ARCHS4_Tissues	http://amp.pharm.mssm.edu/archs4	108
23601	2395	ARCHS4_Cell-lines	http://amp.pharm.mssm.edu/archs4	125
20883	299	ARCHS4_IDG_Coexp	http://amp.pharm.mssm.edu/archs4	352
19612	299	ARCHS4_Kinases_Coexp	http://amp.pharm.mssm.edu/archs4	498
25983	299	ARCHS4_TFs_Coexp	http://amp.pharm.mssm.edu/archs4	1724
19500	137	SysMyo_Muscle_Gene_Sets	http://sys-myo.rhcloud.com/	1135
14893	128	miRTarBase_2017	http://mirtarbase.mbc.nctu.edu.tw/	3240
17598	1208	TargetScan_microRNA_2017	http://www.targetscan.org/	683
5902	109	Enrichr_Libraries_Most_Popular_Genes	http://amp.pharm.mssm.edu/Enrichr	121
12486	299	Enrichr_Submissions_TF-Gene_Coocurrence	http://amp.pharm.mssm.edu/Enrichr	1722
1073	100	Data_Acquisition_Method_Most_Popular_Genes	http://amp.pharm.mssm.edu/Enrichr	12
19513	117	DSigDB	http://tanlab.ucdenver.edu/DSigDB/DSigDBv1.0/	4026
14433	36	GO_Biological_Process_2018	http://www.geneontology.org/	5103
8655	61	GO_Cellular_Component_2018	http://www.geneontology.org/	446
11459	39	GO_Molecular_Function_2018	http://www.geneontology.org/	1151
19741	270	TF_Perturbations_Followed_by_Expression	http://www.ncbi.nlm.nih.gov/geo/	1958
27360	802	Chromosome_Location_hg19	http://hgdownload.cse.ucsc.edu/downloads.html	36
13072	26	NIH_Funded_PIs_2017_Human_GeneRIF	https://www.ncbi.nlm.nih.gov/pubmed/	5687
13464	45	NIH_Funded_PIs_2017_Human_AutoRIF	https://www.ncbi.nlm.nih.gov/pubmed/	12558
13787	200	Rare_Diseases_AutoRIF_ARCHS4_Predictions	https://amp.pharm.mssm.edu/geneshot/	3725
13929	200	Rare_Diseases_GeneRIF_ARCHS4_Predictions	https://www.ncbi.nlm.nih.gov/gene/about-generif	2244
16964	200	NIH_Funded_PIs_2017_AutoRIF_ARCHS4_Predictions	https://www.ncbi.nlm.nih.gov/pubmed/	12558
17258	200	NIH_Funded_PIs_2017_GeneRIF_ARCHS4_Predictions	https://www.ncbi.nlm.nih.gov/pubmed/	5684
10352	58	Rare_Diseases_GeneRIF_Gene_Lists	https://www.ncbi.nlm.nih.gov/gene/about-generif	2244
10471	76	Rare_Diseases_AutoRIF_Gene_Lists	https://amp.pharm.mssm.edu/geneshot/	3725
12419	491	SubCell_BarCode	http://www.subcellbarcode.org/	104
19378	37	GWAS_Catalog_2019	https://www.ebi.ac.uk/gwas	1737
6201	45	WikiPathways_2019_Human	https://www.wikipathways.org/	472
4558	54	WikiPathways_2019_Mouse	https://www.wikipathways.org/	176
3264	22	TRRUST_Transcription_Factors_2019	https://www.grnpedia.org/trrust/	571
7802	92	KEGG_2019_Human	https://www.kegg.jp/	308
8551	98	KEGG_2019_Mouse	https://www.kegg.jp/	303
12444	23	InterPro_Domains_2019	https://www.ebi.ac.uk/interpro/	1071
9000	20	Pfam_Domains_2019	https://pfam.xfam.org/	608
7744	363	DepMap_WG_CRISPR_Screens_Broad_CellLines_2019	https://depmap.org/	558
6204	387	DepMap_WG_CRISPR_Screens_Sanger_CellLines_2019	https://depmap.org/	325
13420	32	MGI_Mammalian_Phenotype_Level_4_2019	http://www.informatics.jax.org/	5261
14148	122	UK_Biobank_GWAS_v1	https://www.ukbiobank.ac.uk/tag/gwas/	857
9813	49	BioPlanet_2019	https://tripod.nih.gov/bioplanet/	1510
1397	13	ClinVar_2019	https://www.ncbi.nlm.nih.gov/clinvar/	182
9116	22	PheWeb_2019	http://pheweb.sph.umich.edu/	1161
17464	63	DisGeNET	https://www.disgenet.org	9828
394	73	HMS_LINCS_KinomeScan	http://lincs.hms.harvard.edu/kinomescan/	148
11851	586	CCLE_Proteomics_2020	https://portals.broadinstitute.org/ccle	378
8189	421	ProteomicsDB_2020	https://www.proteomicsdb.org/	913
18704	100	lncHUB_lncRNA_Co-Expression	https://amp.pharm.mssm.edu/lnchub/	3729
5605	39	Virus-Host_PPI_P-HIPSTer_2020	http://phipster.org/	6715
5718	31	Elsevier_Pathway_Collection	http://www.transgene.ru/disease-pathways/	1721
14156	40	Table_Mining_of_CRISPR_Studies		802
16979	295	COVID-19_Related_Gene_Sets	https://amp.pharm.mssm.edu/covid19	205
4383	146	MSigDB_Hallmark_2020	https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp	50
54974	483	Enrichr_Users_Contributed_Lists_2020	https://maayanlab.cloud/Enrichr	1482
12118	448	TG_GATES_2020	https://toxico.nibiohn.go.jp/english/	1190
12361	124	Allen_Brain_Atlas_10x_scRNA_2021	https://portal.brain-map.org/	766

markers_enriched=list()

if (markers_found) {
  # Convert Seurat names of upregulated marker per cluster to Entrez; use named lists for translation
  marker_genesets_up = sapply(levels(sc$seurat_clusters), function(x) {
    tmp = markers_filt$up %>% dplyr::filter(cluster==x) %>% dplyr::pull(gene)
    tmp = sapply(tmp, function(n) seurat_rowname_to_entrez[[n]][1], USE.NAMES=TRUE, simplify=TRUE) %>% unlist() %>% as.character()
    return(tmp[!is.na(tmp)])
  }, USE.NAMES=TRUE, simplify=TRUE)
  
  # Tests done by Enrichr
  marker_genesets_up_enriched = purrr::map(marker_genesets_up, EnrichrTest, databases=param$enrichr_dbs, padj=param$enrichr_padj)
  
  # Write to files
  marker_genesets_up_enriched_files = purrr::map(names(marker_genesets_up_enriched), function(n) {
    EnrichrWriteResults(enrichr_results=marker_genesets_up_enriched[[n]],
                        file=paste0(param$path_out, "/Functions_marker_up_cluster", n, ".xlsx"))
    })
  
  # Convert Seurat names of downregulated marker per cluster to Entrez; use named lists for translation
  marker_genesets_down = sapply(levels(sc$seurat_clusters), function(x) {
    tmp = markers_filt$down %>% dplyr::filter(cluster==x) %>% dplyr::pull(gene)
    tmp = sapply(tmp, function(x) seurat_rowname_to_entrez[[x]][1], USE.NAMES=TRUE, simplify=TRUE) %>% unlist() %>% as.character()
    return(tmp[!is.na(tmp)])
  }, USE.NAMES=TRUE, simplify=TRUE)
  
  #  Tests done by Enrichr
  marker_genesets_down_enriched = purrr::map(marker_genesets_down, EnrichrTest, databases=param$enrichr_dbs, padj=param$enrichr_padj)

  # Write to files
  marker_genesets_down_enriched_files = purrr::map(names(marker_genesets_down_enriched), function(n) {
    EnrichrWriteResults(enrichr_results=marker_genesets_down_enriched[[n]],
                        file=paste0(param$path_out, "/Functions_marker_down_cluster", n, ".xlsx"))
    })
}

× (Warning)
Warning in .f(.x[[i]], …): Enrichr returns with error:
Warning in .f(.x[[i]], …): Enrichr returns with error:
Warning in .f(.x[[i]], …): Enrichr returns with error:

The following table contains the top enriched terms per cluster.

# Top enriched terms (TODO: better plots, functions)
if (markers_found) {
  cat('#### {.tabset} \n \n')
  
  # Get top ten up over all databases
  marker_genesets_up_top_enriched = purrr::map(marker_genesets_up_enriched, function(enrichr_results) {
    purrr::map_dfr(names(enrichr_results), function(n) {
      enrichr_result_df = cbind(enrichr_results[[n]], list(
        Database = factor(rep(n, nrow(enrichr_results[[n]])), levels=names(enrichr_results)),
        Direction = factor(rep("up", nrow(enrichr_results[[n]])), levels=c("up", "down"))
      ))
      return(enrichr_result_df)
    }) %>% head()
  })
  
  # Get down ten up over all databases
  marker_genesets_down_top_enriched = purrr::map(marker_genesets_down_enriched, function(enrichr_results) {
    purrr::map_dfr(names(enrichr_results), function(n) {
      enrichr_result_df = enrichr_results[[n]]
      if (nrow(enrichr_result_df) > 0 ) {
        enrichr_result_df$Database = n
        enrichr_result_df$Direction = "down"
      } else {
        enrichr_result_df = data.frame(enrichr_result_df, Database=as.character(), Direction=as.character())
      }
      return(enrichr_result_df)
    }) %>% head()
  })
  
  # Join
  marker_genesets_top_enriched = purrr::map(values_to_names(names(marker_genesets_down_top_enriched)), function(n) {
    return(rbind(marker_genesets_up_top_enriched[[n]], marker_genesets_down_top_enriched[[n]]) %>% 
      dplyr::arrange(-Odds.Ratio))
    })
  
  # Print as tabs
  for(n in names(marker_genesets_top_enriched)){
    cat('##### ', n, ' \n')
    
    cat(knitr::kable(marker_genesets_top_enriched[[n]][, c("Database", "Term", "Direction", "Adjusted.P.value", "Odds.Ratio")], 
                     align="l", caption="Top ten enriched terms per geneset", format="html") %>% 
    kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>% 
    kableExtra::scroll_box(width="100%"))

    cat(' \n \n')
  }
  cat(' \n \n')
}

1

Top ten enriched terms per geneset

Database	Term	Direction	Adjusted.P.value	Odds.Ratio
GO_Molecular_Function_2018	MHC class I protein binding (GO:0042288)	up	0.0026977	208.020833
GO_Molecular_Function_2018	MHC protein binding (GO:0042287)	up	0.0042620	89.080357
GO_Molecular_Function_2018	serine-type endopeptidase activity (GO:0004252)	up	0.0066157	20.506736
GO_Molecular_Function_2018	serine-type peptidase activity (GO:0008236)	up	0.0073965	18.216590
GO_Molecular_Function_2018	endopeptidase activity (GO:0004175)	up	0.0042620	16.026122
GO_Molecular_Function_2018	protein homodimerization activity (GO:0042803)	up	0.0165146	8.364502

2

Top ten enriched terms per geneset

Database	Term	Direction	Adjusted.P.value	Odds.Ratio
GO_Molecular_Function_2018	MAP kinase tyrosine/serine/threonine phosphatase activity (GO:0017017)	up	0.0071955	1110.555556
GO_Molecular_Function_2018	MAP kinase phosphatase activity (GO:0033549)	up	0.0071955	832.833333
GO_Molecular_Function_2018	MHC class I protein binding (GO:0042288)	up	0.0074593	512.384615
GO_Molecular_Function_2018	MHC protein binding (GO:0042287)	up	0.0108530	229.505747
GO_Molecular_Function_2018	protein tyrosine/serine/threonine phosphatase activity (GO:0008138)	up	0.0108530	201.646465
GO_Molecular_Function_2018	MHC class II receptor activity (GO:0032395)	down	0.0000180	113.236467
GO_Molecular_Function_2018	MHC class II protein complex binding (GO:0023026)	down	0.0000064	77.852665
GO_Molecular_Function_2018	immunoglobulin binding (GO:0019865)	down	0.0017802	63.149364
GO_Molecular_Function_2018	MHC protein complex binding (GO:0023023)	down	0.0000084	61.160714
GO_Molecular_Function_2018	phosphoprotein phosphatase activity (GO:0004721)	up	0.0359009	49.039506
GO_Molecular_Function_2018	exopeptidase activity (GO:0008238)	down	0.0064905	16.542005
GO_Molecular_Function_2018	transcription factor activity, RNA polymerase II core promoter proximal region sequence-specific binding (GO:0000982)	down	0.0105850	5.103332

3

Top ten enriched terms per geneset

Database	Term	Direction	Adjusted.P.value	Odds.Ratio
GO_Molecular_Function_2018	mRNA 3’-UTR AU-rich region binding (GO:0035925)	up	0.0282789	399.68000
GO_Molecular_Function_2018	AU-rich element binding (GO:0017091)	up	0.0282789	199.74000
GO_Molecular_Function_2018	MHC protein complex binding (GO:0023023)	down	0.0000032	156.44706
GO_Molecular_Function_2018	MHC class II receptor activity (GO:0032395)	down	0.0020105	138.56944
GO_Molecular_Function_2018	MHC class II protein complex binding (GO:0023026)	down	0.0001341	131.53187
GO_Molecular_Function_2018	mRNA 3’-UTR binding (GO:0003730)	up	0.0470058	64.29677
GO_Molecular_Function_2018	cytokine receptor activity (GO:0004896)	up	0.0470058	55.33889
GO_Biological_Process_2018	cellular response to tumor necrosis factor (GO:0071356)	up	0.0162858	51.56771
GO_Molecular_Function_2018	oxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor (GO:0016616)	up	0.0470058	45.76322
GO_Molecular_Function_2018	serine-type endopeptidase activity (GO:0004252)	down	0.0008508	15.68380
GO_Molecular_Function_2018	serine-type peptidase activity (GO:0008236)	down	0.0008849	13.91614
GO_Molecular_Function_2018	endopeptidase activity (GO:0004175)	down	0.0008849	10.56789

4

Top ten enriched terms per geneset

Database	Term	Direction	Adjusted.P.value	Odds.Ratio
GO_Molecular_Function_2018	MHC class II receptor activity (GO:0032395)	up	0.0000003	289.043478
GO_Molecular_Function_2018	MHC class II protein complex binding (GO:0023026)	up	0.0000000	271.909091
GO_Molecular_Function_2018	MHC protein complex binding (GO:0023023)	up	0.0000000	209.129371
GO_Molecular_Function_2018	T cell receptor binding (GO:0042608)	down	0.0007908	179.126126
GO_Biological_Process_2018	antigen processing and presentation of exogenous peptide antigen (GO:0002478)	up	0.0000000	49.544900
GO_Molecular_Function_2018	disordered domain specific binding (GO:0097718)	down	0.0159422	26.845946
GO_Molecular_Function_2018	transcription factor activity, RNA polymerase II distal enhancer sequence-specific binding (GO:0003705)	up	0.0158176	20.811650
GO_Molecular_Function_2018	amyloid-beta binding (GO:0001540)	down	0.0128799	16.033131
GO_Molecular_Function_2018	actin binding (GO:0003779)	down	0.0116168	6.025464
GO_Molecular_Function_2018	cadherin binding (GO:0045296)	down	0.0159422	4.845283
GO_Molecular_Function_2018	protein homodimerization activity (GO:0042803)	up	0.0286006	4.780397
GO_Molecular_Function_2018	protein homodimerization activity (GO:0042803)	down	0.0159422	3.470588

5

Top ten enriched terms per geneset

Database	Term	Direction	Adjusted.P.value	Odds.Ratio
GO_Molecular_Function_2018	T cell receptor binding (GO:0042608)	down	0.0001246	174.385965
GO_Molecular_Function_2018	ubiquitin-protein transferase inhibitor activity (GO:0055105)	down	0.0001246	174.385965
GO_Molecular_Function_2018	Toll-like receptor binding (GO:0035325)	up	0.0013078	59.550189
GO_Molecular_Function_2018	rRNA binding (GO:0019843)	down	0.0000005	35.083081
GO_Molecular_Function_2018	phosphotyrosine residue binding (GO:0001784)	up	0.0044074	15.551610
GO_Molecular_Function_2018	protein phosphorylated amino acid binding (GO:0045309)	up	0.0053564	12.867032
GO_Molecular_Function_2018	mRNA binding (GO:0003729)	down	0.0000065	10.901952
GO_Molecular_Function_2018	RNA binding (GO:0003723)	down	0.0000000	8.320770
GO_Molecular_Function_2018	kinase binding (GO:0019900)	down	0.0000506	6.011728
GO_Molecular_Function_2018	protein heterodimerization activity (GO:0046982)	up	0.0053564	3.925270
GO_Molecular_Function_2018	transition metal ion binding (GO:0046914)	up	0.0053564	3.194749
GO_Molecular_Function_2018	protein homodimerization activity (GO:0042803)	up	0.0020329	2.922092

6

Top ten enriched terms per geneset

Database	Term	Direction	Adjusted.P.value	Odds.Ratio
GO_Molecular_Function_2018	MHC class II receptor activity (GO:0032395)	down	0.0005745	293.72059
GO_Molecular_Function_2018	MHC class II protein complex binding (GO:0023026)	down	0.0000157	288.00000
GO_Biological_Process_2018	granzyme-mediated apoptotic signaling pathway (GO:0008626)	up	0.0042951	269.68919
GO_Molecular_Function_2018	MHC protein complex binding (GO:0023023)	down	0.0000157	233.96484
GO_Molecular_Function_2018	beta-galactosidase activity (GO:0004565)	down	0.0284356	221.95556
GO_Biological_Process_2018	lymphocyte mediated immunity (GO:0002449)	up	0.0093912	119.83183
GO_Molecular_Function_2018	MHC protein complex binding (GO:0023023)	up	0.0003120	103.88021
GO_Molecular_Function_2018	MHC protein binding (GO:0042287)	down	0.0041184	83.83613
GO_Biological_Process_2018	cellular defense response (GO:0006968)	up	0.0004909	43.75604
GO_Molecular_Function_2018	cytokine receptor activity (GO:0004896)	down	0.0194262	32.99089
GO_Biological_Process_2018	regulation of immune response (GO:0050776)	up	0.0000000	28.21577
GO_Biological_Process_2018	apoptotic process (GO:0006915)	up	0.0047921	12.84162

7

Top ten enriched terms per geneset

Database	Term	Direction	Adjusted.P.value	Odds.Ratio
GO_Biological_Process_2018	response to interleukin-4 (GO:0070670)	up	0.0018866	475.690476
GO_Biological_Process_2018	cellular response to interleukin-4 (GO:0071353)	up	0.0018866	475.690476
GO_Biological_Process_2018	positive regulation of interleukin-12 production (GO:0032735)	up	0.0104144	144.659420
GO_Molecular_Function_2018	phospholipase inhibitor activity (GO:0004859)	down	0.0002426	143.920482
GO_Biological_Process_2018	beta-catenin-TCF complex assembly (GO:1904837)	up	0.0109625	118.797619
GO_Biological_Process_2018	regulation of phosphatidylinositol 3-kinase activity (GO:0043551)	up	0.0109625	107.284946
GO_Molecular_Function_2018	MHC protein complex binding (GO:0023023)	down	0.0001011	64.712195
GO_Molecular_Function_2018	MHC class II protein complex binding (GO:0023026)	down	0.0017751	55.331789
GO_Biological_Process_2018	canonical Wnt signaling pathway (GO:0060070)	up	0.0364803	46.748826
GO_Molecular_Function_2018	cadherin binding involved in cell-cell adhesion (GO:0098641)	down	0.0024344	44.950301
GO_Molecular_Function_2018	protein binding involved in cell-cell adhesion (GO:0098632)	down	0.0027672	39.951807
GO_Molecular_Function_2018	cadherin binding (GO:0045296)	down	0.0000172	9.524592

8

Top ten enriched terms per geneset

Database	Term	Direction	Adjusted.P.value	Odds.Ratio
GO_Molecular_Function_2018	urea transmembrane transporter activity (GO:0015204)	up	0.0376939	285.42857
GO_Molecular_Function_2018	MAP kinase tyrosine/serine/threonine phosphatase activity (GO:0017017)	up	0.0376939	237.84524
GO_Molecular_Function_2018	MHC class II receptor activity (GO:0032395)	down	0.0009057	217.03261
GO_Molecular_Function_2018	MHC class II protein complex binding (GO:0023026)	down	0.0000445	209.39161
GO_Molecular_Function_2018	MHC protein complex binding (GO:0023023)	down	0.0000445	170.10511
GO_Molecular_Function_2018	collagen binding (GO:0005518)	down	0.0006591	55.45269
GO_Molecular_Function_2018	RNA polymerase II regulatory region DNA binding (GO:0001012)	up	0.0270704	24.98359
GO_Molecular_Function_2018	serine-type endopeptidase activity (GO:0004252)	down	0.0010400	19.62599
GO_Molecular_Function_2018	transcriptional activator activity, RNA polymerase II transcription regulatory region sequence-specific binding (GO:0001228)	up	0.0270704	17.53025
GO_Molecular_Function_2018	serine-type peptidase activity (GO:0008236)	down	0.0013527	17.42416
GO_Molecular_Function_2018	transcription regulatory region sequence-specific DNA binding (GO:0000976)	up	0.0270704	17.03806
GO_Molecular_Function_2018	RNA polymerase II regulatory region sequence-specific DNA binding (GO:0000977)	up	0.0376939	10.68271

9

Top ten enriched terms per geneset

Database	Term	Direction	Adjusted.P.value	Odds.Ratio
GO_Molecular_Function_2018	T cell receptor binding (GO:0042608)	down	0.0002190	209.463158
GO_Molecular_Function_2018	ubiquitin-protein transferase inhibitor activity (GO:0055105)	down	0.0136554	103.635417
GO_Molecular_Function_2018	cysteine-type peptidase activity (GO:0008234)	up	0.0023542	10.624697
GO_Molecular_Function_2018	cysteine-type endopeptidase activity (GO:0004197)	up	0.0011493	9.927904
GO_Molecular_Function_2018	mRNA binding (GO:0003729)	down	0.0122091	7.437422
GO_Molecular_Function_2018	protease binding (GO:0002020)	up	0.0076689	6.606191
GO_Molecular_Function_2018	kinase binding (GO:0019900)	down	0.0025145	5.429479
GO_Molecular_Function_2018	RNA binding (GO:0003723)	down	0.0000076	4.411334
GO_Molecular_Function_2018	protein kinase binding (GO:0019901)	down	0.0202875	4.039950
GO_Molecular_Function_2018	kinase binding (GO:0019900)	up	0.0011493	4.011993
GO_Molecular_Function_2018	protein kinase binding (GO:0019901)	up	0.0011493	3.861584
GO_Molecular_Function_2018	protein homodimerization activity (GO:0042803)	up	0.0011493	3.400355

10

Top ten enriched terms per geneset

Database	Term	Direction	Adjusted.P.value	Odds.Ratio
GO_Molecular_Function_2018	mRNA 5’-UTR binding (GO:0048027)	down	0.0000000	33.073659
GO_Molecular_Function_2018	rRNA binding (GO:0019843)	down	0.0000000	17.288046
GO_Molecular_Function_2018	translation factor activity, RNA binding (GO:0008135)	down	0.0000000	12.580069
GO_Molecular_Function_2018	RNA binding (GO:0003723)	down	0.0000000	9.787102
GO_Molecular_Function_2018	mRNA binding (GO:0003729)	down	0.0000000	9.346812
GO_Molecular_Function_2018	actin filament binding (GO:0051015)	up	0.0000837	7.121103
GO_Molecular_Function_2018	cadherin binding (GO:0045296)	down	0.0000000	6.300408
GO_Molecular_Function_2018	actin binding (GO:0003779)	up	0.0000036	5.620882
GO_Molecular_Function_2018	GTPase activity (GO:0003924)	up	0.0004073	4.603610
GO_Molecular_Function_2018	ubiquitin protein ligase binding (GO:0031625)	up	0.0004073	4.099573
GO_Molecular_Function_2018	cadherin binding (GO:0045296)	up	0.0004073	3.952044
GO_Molecular_Function_2018	kinase binding (GO:0019900)	up	0.0004073	3.476703

11

Top ten enriched terms per geneset

Database	Term	Direction	Adjusted.P.value	Odds.Ratio
GO_Molecular_Function_2018	T cell receptor binding (GO:0042608)	down	0.0000002	664.466667
GO_Molecular_Function_2018	MHC class II receptor activity (GO:0032395)	up	0.0000180	113.236467
GO_Molecular_Function_2018	RAGE receptor binding (GO:0050786)	down	0.0110879	91.838710
GO_Molecular_Function_2018	Toll-like receptor binding (GO:0035325)	down	0.0110879	91.838710
GO_Molecular_Function_2018	MHC class I protein binding (GO:0042288)	down	0.0008597	89.083457
GO_Molecular_Function_2018	MHC class II protein complex binding (GO:0023026)	up	0.0000064	77.852665
GO_Molecular_Function_2018	immunoglobulin binding (GO:0019865)	up	0.0017802	63.149364
GO_Molecular_Function_2018	MHC protein complex binding (GO:0023023)	up	0.0000084	61.160714
GO_Molecular_Function_2018	MHC protein binding (GO:0042287)	down	0.0061629	36.264117
GO_Molecular_Function_2018	SH3 domain binding (GO:0017124)	down	0.0188089	18.805801
GO_Molecular_Function_2018	exopeptidase activity (GO:0008238)	up	0.0064905	16.542005
GO_Molecular_Function_2018	transcription factor activity, RNA polymerase II core promoter proximal region sequence-specific binding (GO:0000982)	up	0.0105850	5.103332

Differentially expressed genes

If requested, we identify genes that are differentially expressed between two groups of cells. Groups can be defined by columns in the cell metadata. Different types of tests can be used and input data for testing can be the different assays as well as the computed dimensionality reductions. Resulting p-values are adjusted using the Bonferroni method. The names of differentially expressed genes per cluster, alongside statistical measures and additional gene annotation are written to file.

# We first compute the DEGs for all requested contrasts

# Prepare a list with contrasts (input can be R data.frame table or Excel file)
degs_contrasts_list = DegsSetupContrastsList(sc, param$deg_contrasts, param$latent_vars)

# Add the actual data to the list
degs_contrasts_list = purrr::map(degs_contrasts_list, function(contrast){
  # If there were already errors, just return
  if (length(contrast[["error_messages"]]) > 0) return(c(contrast, list(object=NULL, cells_group1_idx_subset=as.integer(), cells_group2_idx_subset=as.integer())))
  
  # Get cells indices
  cells_group1_idx = contrast[["cells_group1_idx"]]
  cells_group2_idx = contrast[["cells_group2_idx"]]

  # Create object
  if (contrast[["use_reduction"]]) {
    # Use dimensionality reduction
    contrast[["object"]] = Seurat::Reductions(sc, slot=contrast[["assay"]])
  } else {
    # Use assay
    contrast[["object"]] = Seurat::GetAssay(sc[,unique(c(cells_group1_idx,cells_group2_idx))], assay=contrast[["assay"]])
    
    # This saves a lot of memory for parallelisation
    if (contrast[["slot"]]!="scale.data") contrast[["object"]]@scale.data = new(Class = 'matrix')
  }
  
  # Variable latent vars must be passed as data.frame
  if (!is.null(contrast[["latent_vars"]]) && length(contrast[["latent_vars"]]) > 0) contrast[["latent_vars"]] = sc[[unique(c(cells_group1_idx,cells_group2_idx)), contrast[["latent_vars"]], drop=FALSE]]
  
  # Now update cell indices so that they match to subset
  contrast[["cells_group1_idx_subset"]] = match(colnames(sc)[cells_group1_idx], colnames(contrast[["object"]]))
  contrast[["cells_group2_idx_subset"]] = match(colnames(sc)[cells_group2_idx], colnames(contrast[["object"]]))
  
  return(contrast)
})

# Compute the tests; TODO: this chunk may be done in parallel in future
degs_contrasts_results = purrr::map(degs_contrasts_list, function(contrast) {
  if (length(contrast$error_messages)==0) {
    # No errors, do contrast
    test_results = DegsTestCellSets(object=contrast[["object"]],
                                   slot=contrast[["slot"]],
                                   cells_1=colnames(contrast[["object"]])[contrast[["cells_group1_idx_subset"]]],
                                   cells_2=colnames(contrast[["object"]])[contrast[["cells_group2_idx_subset"]]],
                                   is_reduction=contrast[["use_reduction"]],
                                   logfc.threshold=contrast[["log2FC"]],
                                   test.use=contrast[["test"]],
                                   min.pct=contrast[["min_pct"]],
                                   latent.vars=contrast[["latent_vars"]])
  } else {
    # Errors, return empty data.frame
    test_results = DegsEmptyResultsTable()
  }
  
  # Sort and filter table
  test_results = test_results %>% DegsSort() %>% DegsFilter(contrast[["log2FC"]], contrast[["padj"]], split_by_dir=FALSE)

  # Add mean gene expression data (counts or data, dep on slot)
  avg.1 = DegsAvgData(contrast[["object"]], cells=contrast[["cells_group1_idx_subset"]], genes=test_results$gene, slot=contrast[["slot"]])[,1]
  avg.2 = DegsAvgData(contrast[["object"]], cells=contrast[["cells_group2_idx_subset"]], genes=test_results$gene, slot=contrast[["slot"]])[,1]
  test_results = cbind(test_results, avg.1, avg.2)
  
  # Add test results and drop unneccessary data
  contrast = c(contrast, list(results=test_results))
  contrast[["object"]] = NULL
  contrast[["cells_group1_idx_subset"]] = NULL
  contrast[["cells_group2_idx_subset"]] = NULL
  
  return(contrast)
})

# Also remove objects from deg_contrasts_list (save memory)
degs_contrasts_list = purrr::map(degs_contrasts_list, function(contrast){ contrast[["object"]] = NULL; return(contrast)})

# Compute enrichr results (not in parallel due to server load)
degs_contrasts_results = purrr::map(degs_contrasts_results, function(contrast) {
  # Get results table
  results_table = contrast$results
  
  # Drop existing results
  if ("enrichr" %in% names(contrast)) d[["enrichr"]] = NULL
  
  # Split into up- and downregulated DEGs, then translate to Entrez gene, runEnrichr
  degs_up = dplyr::filter(results_table, avg_log2FC > 0) %>% dplyr::pull(gene) %>% unique()
  degs_up = sapply(degs_up, function(n) seurat_rowname_to_entrez[[n]][1], USE.NAMES=TRUE, simplify=TRUE) %>% unlist() %>% as.character()
  degs_up = degs_up[!is.na(degs_up)]
  enrichr_results_up = EnrichrTest(genes=degs_up, databases=param$enrichr_dbs, padj=param$enrichr_padj)
  
  degs_down = dplyr::filter(results_table, avg_log2FC < 0) %>% dplyr::pull(gene) %>% unique()
  degs_down = sapply(degs_down, function(n) seurat_rowname_to_entrez[[n]][1], USE.NAMES=TRUE, simplify=TRUE) %>% unlist() %>% as.character()
  degs_down = degs_down[!is.na(degs_down)]
  enrichr_results_down = EnrichrTest(genes=degs_down, databases=param$enrichr_dbs, padj=param$enrichr_padj)

  
  # Flatten both enrichr results into tables
  enrichr_results_up = purrr::map_dfr(names(enrichr_results_up), function(n) {
    return(cbind(enrichr_results_up[[n]], 
          list(Database=factor(rep(n, nrow(enrichr_results_up[[n]])), levels=names(enrichr_results_up)), 
               Direction=factor(rep("up", nrow(enrichr_results_up[[n]])), levels=c("up", "down"))
               )
          ))
  })
  
  enrichr_results_down = purrr::map_dfr(names(enrichr_results_down), function(n) {
    return(cbind(enrichr_results_down[[n]], 
          list(Database=factor(rep(n, nrow(enrichr_results_down[[n]])), levels=names(enrichr_results_down)), 
               Direction=factor(rep("up", nrow(enrichr_results_down[[n]])), levels=c("up", "down"))
               )
          ))
  })
  
  # Rbind and add factor levels
  enrichr_results = dplyr::bind_rows(enrichr_results_up, enrichr_results_down)
  return(c(contrast, list(enrichr=enrichr_results)))
})

# Now regroup list so that subsets are together again
original_contrast_rows = purrr::map_int(degs_contrasts_results, function(contrast){return(contrast[["contrast_row"]]) })
degs = split(degs_contrasts_results, original_contrast_rows)

# Write degs to files
degs_result_files = purrr::map_chr(degs, function(degs_subsets) {
  # The output file
  file = paste0(param$path_out, "/degs_contrast_row_", degs_subsets[[1]][["contrast_row"]], "_results.xlsx")
  
  # Write degs
  degs_subsets_results = purrr::map(degs_subsets, function(contrast) {return(contrast[["results"]])})
  names(degs_subsets_results) = purrr::map_chr(degs_subsets, function(contrast) {return(ifelse(!is.na(contrast[["subset_group"]]), contrast[["subset_group"]], "All"))})
  file = DegsWriteToFile(degs_subsets_results, 
                         annot_ensembl=annot_ensembl,
                         gene_to_ensembl=seurat_rowname_to_ensembl,
                         file=file,
                         additional_readme=NULL)
  
  return(file)
})

# Write enrichr results to files
degs_enrichr_files = purrr::map_chr(degs, function(degs_subsets) {
  # The output file
  file = paste0(param$path_out, "/degs_contrast_row_", degs_subsets[[1]][["contrast_row"]], "_functions.xlsx")
  
  # Write enrichr results
  degs_subsets_enrichr = purrr::map(degs_subsets, function(contrast) {return(contrast[["enrichr"]])})
  names(degs_subsets_enrichr) = purrr::map_chr(degs_subsets, function(contrast) {return(ifelse(!is.na(contrast[["subset_group"]]), contrast[["subset_group"]], "All"))})
  file = EnrichrWriteResults(degs_subsets_enrichr, file=file)
  
  return(file)
})

knitr_header_string = '

## {{condition_column}}: {{condition_group1}} vs {{condition_group2}}

General configuration:

* assay: {{assay}}
* slot: {{slot}}
* test: {{test}}
* maximum adjusted p-value: {{padj}}
* minimum absolute log2 foldchange: {{log2FC}}
* minimum percentage of cells: {{min_pct}}

Subset on column: \'{{subset_column}}\''

if (length(degs)==0) message("No DEG contrasts specified.")

for (i in seq(degs)) {
  # Get subsets
  degs_subsets = degs[[i]]
  first_contrast = degs_subsets[[1]]
  
  # Create header
  cat(
    knitr::knit_expand(text = knitr_header_string,
                     condition_column=first_contrast[["condition_column"]],
                     condition_group1=first_contrast[["condition_group1"]],
                     condition_group2=first_contrast[["condition_group2"]],
                     assay=first_contrast[["assay"]],
                     slot=first_contrast[["slot"]],
                     test=first_contrast[["test"]],
                     padj=first_contrast[["padj"]],
                     log2FC=first_contrast[["log2FC"]],
                     min_pct=first_contrast[["min_pct"]],
                     subset_column=ifelse(is.na(first_contrast[["subset_column"]]), "-", first_contrast[["subset_column"]]))
  , '\n')
  
  # Get error messages
  error_messages = unique(purrr::flatten_chr(purrr::map(degs_subsets, function(contrast){return(contrast[["error_messages"]])})))

  # Create combined results table
  degs_subsets_results = purrr::map_dfr(degs_subsets, function(contrast) {
    subset_group_value = ifelse(!is.na(contrast[["subset_group"]]), contrast[["subset_group"]], "All")
    results_table = cbind(contrast[["results"]],
                          list(subset_group=factor(rep(subset_group_value, nrow(contrast[["results"]])), levels=c(subset_group_value)),
                               cells1=rep(length(contrast[["cells_group1_idx"]]), nrow(contrast[["results"]])),
                               cells2=rep(length(contrast[["cells_group2_idx"]]), nrow(contrast[["results"]]))
                        )
                   )
    return(results_table)
  }) %>% tibble::as_tibble()

  # Print warnings/errors
  if (length(error_messages) > 0) {
    warning(error_messages)
  }
  
  # Print summary table
  cat(
      knitr::kable(degs_subsets_results %>% 
                   dplyr::group_by(subset_group) %>% 
                   dplyr::summarise(Cells1=dplyr::first(cells1), 
                                    Cells2=dplyr::first(cells2),
                                    DEGs=length(gene),
                                    DEGs_up=sum(avg_log2FC > 0),
                                    DEGs_down=sum(avg_log2FC < 0)),
                   align="l", caption="DEG summary", col.names=c("Subset", "Cells in group 1", "Cells in group 2", "# DEGs", "# DEGs upregulated", "# DEGs downregulated"), format="html") %>%
        kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))
    )
  cat('\n \n')
} 

orig.ident: pbmc_10x vs pbmc_smartseq2_sample1

General configuration:

• assay: RNA
• slot: data
• test: wilcox
• maximum adjusted p-value: 0.05
• minimum absolute log2 foldchange: 0
• minimum percentage of cells: 0.1

Subset on column: ‘-’

DEG summary

Subset	Cells in group 1	Cells in group 2	# DEGs	# DEGs upregulated	# DEGs downregulated
All	3608	310	3417	1150	2267

orig.ident: pbmc_10x vs pbmc_smartseq2_sample1

General configuration:

• assay: RNA
• slot: data
• test: wilcox
• maximum adjusted p-value: 0.05
• minimum absolute log2 foldchange: 0
• minimum percentage of cells: 0.1

Subset on column: ‘seurat_clusters’

DEG summary

Subset	Cells in group 1	Cells in group 2	# DEGs	# DEGs upregulated	# DEGs downregulated
1	704	66	1777	589	1188
2	577	48	1418	459	959
3	578	34	1281	394	887
4	410	24	1046	295	751
5	369	44	1452	495	957
6	305	32	1030	344	686
7	251	18	967	228	739
8	202	15	848	157	691
9	122	8	364	56	308
10	51	16	42	7	35
11	39	5	99	0	99

Phase: G1 vs G2M

General configuration:

• assay: RNA
• slot: data
• test: wilcox
• maximum adjusted p-value: 0.05
• minimum absolute log2 foldchange: 0
• minimum percentage of cells: 0.1

Subset on column: ‘seurat_clusters’

DEG summary

Subset	Cells in group 1	Cells in group 2	# DEGs	# DEGs upregulated	# DEGs downregulated
1	275	229	6	0	6
2	214	189	7	0	7

Output

Export to Loupe Cell Browser

We export the UMAP 2D visualisation, metadata such as the cell clusters, and lists of differentially expressed genes, so you can open and work with these in the Loupe Cell Browser.

# Export UMAP coordinates
loupe_umap = as.data.frame(sc@reductions$umap@cell.embeddings)
loupe_umap = cbind(Barcode=rownames(loupe_umap), loupe_umap)
colnames(loupe_umap) = c("Barcode", "UMAP-1", "UMAP-2")
write.table(loupe_umap, file=paste0(param$path_out, "/Seurat2Loupe_umap.csv"), col.names=TRUE, row.names=FALSE, quote=FALSE, sep=",")

# Export categorical metadata
loupe_meta = as.data.frame(sc@meta.data)
idx_keep = sapply(1:ncol(loupe_meta), function(x) !is.numeric(loupe_meta[,x]))
loupe_meta = cbind(Barcode=rownames(loupe_meta), loupe_meta[, idx_keep])
write.table(x=loupe_meta, file=paste0(param$path_out, "/Seurat2Loupe_metadata.csv"), col.names=TRUE, row.names=FALSE, quote=FALSE, sep=",")

# Export gene sets
loupe_genesets = data.frame(List=sapply(markers_filt$up[,"cluster"], function(s) paste0("DEG_up_cluster_", s)), 
                            Name=markers_filt$up[,"gene"], 
                            Ensembl=ifelse(markers_filt$up[,"gene"] %in% names(seurat_rowname_to_ensembl),
                                           seurat_rowname_to_ensembl[markers_filt$up[,"gene"]], markers_filt$up[,"gene"]))
                            
loupe_genesets = rbind(loupe_genesets, 
                       data.frame(List=sapply(markers_filt$down[,"cluster"], function(s) paste0("DEG_down_cluster_", s)), 
                                  Name=markers_filt$down[,"gene"],
                                  Ensembl=ifelse(markers_filt$down[,"gene"] %in% names(seurat_rowname_to_ensembl),
                                           seurat_rowname_to_ensembl[markers_filt$down[,"gene"]], markers_filt$down[,"gene"])))

genesets_to_export = list(genes_cc_s_phase=genes_s[,2], genes_cc_g2m_phase=genes_g2m[,2])
for (i in names(genesets_to_export)) {
  tmp_genes = genesets_to_export[[i]]
  tmp_genes = tmp_genes[tmp_genes %in% names(symbol_to_ensembl)]
  loupe_genesets = rbind(loupe_genesets,
                         data.frame(List=i,
                                    Name=tmp_genes,
                                    Ensembl=ifelse(tmp_genes %in% names(seurat_rowname_to_ensembl), 
                                                   seurat_rowname_to_ensembl[tmp_genes], tmp_genes)))
}

write.table(loupe_genesets, file=paste0(param$path_out, "/Seurat2Loupe_genesets.csv"), col.names=TRUE, row.names=FALSE, quote=FALSE, sep=",")

Export to the Cerebro Browser

We export the assay data, clustering, visualisation, marker genes and enriched pathways in a format that can be read by the Cerebro Browser (romanhaa 2021)

# Top expressed genes
sc = cerebroApp::getMostExpressedGenes(sc, 
                                       assay=Seurat::DefaultAssay(sc),
                                       column_sample="orig.ident",
                                       column_cluster="seurat_clusters")

gene_lists_for_cerebro = list()
if (markers_found) {
  gene_lists_for_cerebro = split(markers_filt$all$gene, markers_filt$all$cluster)
  names(gene_lists_for_cerebro) = paste("Marker cluster", names(gene_lists_for_cerebro))
}
gene_lists_for_cerebro[["G2M_phase_genes"]] = genes_g2m[, 2]
gene_lists_for_cerebro[["S_phase_genes"]] = genes_s[, 2]
gene_lists_for_cerebro[["mitochondrial_genes"]] = grep(param$mt, rownames(sc), v=TRUE)
cerebro_species = gsub("_gene_ensembl", "", param$mart_dataset)
cerebro_species = ifelse(grepl("sapiens", cerebro_species), "Hg", ifelse(grepl("musculus", cerebro_species), "Mm", cerebro_species))

res = ExportToCerebro(sc=sc, 
                      path=paste0(param$path_out,"/cerebro.crb"), 
                      param=param, 
                      project=param$project_id, 
                      species=cerebro_species, 
                      assay=DefaultAssay(sc), 
                      column_sample="orig.ident", 
                      column_cluster="seurat_clusters", 
                      column_ccphase="Phase", 
                      gene_lists=gene_lists_for_cerebro, 
                      marker_genes=markers_filt$all, 
                      enriched_pathways=enriched)

Output files

All files generated with this report are written into the provided output folder test_datasets/10x_SmartSeq2_pbmc_GSE132044/results/:

• Annotation files
  • hsapiens_gene_ensembl.v98.annot.txt: Table that contains several identifiers and annotation (columns) per gene (rows) (optional)
    
• Differentially expressed genes
  • degs_cluster_vs_rest.xlsx: Excel file with one tab per cell cluster
    
• Functional enrichment of differentially expressed genes per cell cluster
  • Functions_DEG_down_cluster_1.xlsx, Functions_DEG_up_cluster_1.xlsx, …: Excel files with one tab per database
• Loupe Cell Browser files
  • Seurat2Loupe_umap.csv: Seurat UMAP 2D visualisation
    Import to Loupe through “Import Projection”
    
  • Seurat2Loupe_metadata.csv: Seurat categorial meta data including clusters and cell cycle phases
    Import to Loupe through “Import Categories”
    
  • Seurat2Loupe_genesets.csv: Seurat differentially expressed genes
    Import to Loupe through “Import Lists”
    
• Cerebro files:
  • cerebro.crb: Can be loaded into the Cerebro Browser

# Add colors to the sc object for compatibility with downstream tools
# List names should correspond to meta-data columns
# This could be done for other meta-data as well 
sc@misc$colors = list("orig.ident" = param$col_samples,
                      "seurat_clusters" = param$col_clusters)

save.image(file=paste0(param$path_out, "/scrnaseq.RData"))

Parameters and software versions

The following parameters were used to run the workflow.

out = scrnaseq_params_info(params=param)

knitr::kable(out, align="l") %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"), full_width=FALSE, position="left")

Name	Value
project_id	pbmc
path_data	name:pbmc_10x, pbmc_smartseq2; type:10x, smartseq2; path:test_datasets/10x_SmartSeq2_pbmc_GSE132044/counts/10x/, test_datasets/10x_SmartSeq2_pbmc_GSE132044/counts/smartseq2/counts_table.tsv.gz; stats:NA, NA
path_out	test_datasets/10x_SmartSeq2_pbmc_GSE132044/results/
file_known_markers	test_datasets/10x_pbmc_1k_healthyDonor_v3Chemistry/known_markers.xlsx
mart_dataset	hsapiens_gene_ensembl
annot_version	98
annot_main	ensembl=ensembl_gene_id, symbol=external_gene_name, entrez=entrezgene_accession
mart_attributes	ensembl_gene_id, external_gene_name, entrezgene_accession, chromosome_name, start_position, end_position, percentage_gene_gc_content, gene_biotype, strand, description
mt	^MT-
cell_filter	pbmc_10x:nFeature_RNA=c(200, NA), percent_mt=c(NA, 20); pbmc_smartseq2_sample1:nFeature_RNA=c(200, NA), percent_mt=c(NA, 20)
feature_filter	pbmc_10x:min_counts=1, min_cells=3; pbmc_smartseq2_sample1:min_counts=1, min_cells=3
samples_min_cells	10
norm	RNA
cc_remove	FALSE
cc_remove_all	FALSE
cc_rescore_after_merge	TRUE
integrate_samples	method:standard; reference_dataset:1; dimensions:30
pc_n	10
cluster_resolution	0.5
marker_padj	0.05
marker_log2FC	1
marker_pct	0.25
deg_contrasts	condition_column:orig.ident, orig.ident, Phase; condition_group1:pbmc_10x, pbmc_10x, G1; condition_group2:pbmc_smartseq2_sample1, pbmc_smartseq2_sample1, G2M; subset_column:NA, seurat_clusters, seurat_clusters; subset_group:NA, , 1;2
enrichr_padj	0.05
enrichr_dbs	GO_Molecular_Function_2018, GO_Biological_Process_2018, GO_Cellular_Component_2018
col	palevioletred
col_palette_samples	ggsci::pal_jama
col_palette_clusters	ggsci::pal_startrek
path_to_git	.
debugging_mode	default_debugging
file_annot	test_datasets/10x_SmartSeq2_pbmc_GSE132044/results//hsapiens_gene_ensembl.v98.annot.txt
col_samples	pbmc_10x=#374E55FF, pbmc_smartseq2_sample1=#DF8F44FF
col_clusters	1=#CC0C00FF, 2=#5C88DAFF, 3=#84BD00FF, 4=#FFCD00FF, 5=#7C878EFF, 6=#00B5E2FF, 7=#00AF66FF, 8=#CC0C00B2, 9=#5C88DAB2, 10=#84BD00B2, 11=#FFCD00B2

This report was generated using the scrnaseq GitHub repository. Software versions were collected at run time.

out = scrnaseq_session_info(param$path_to_git)

knitr::kable(out, align="l") %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))

Name	Version
ktrns/scrnaseq	1eab4da6210401d2ae47032989bc3a63b1bedfa0
R	R version 3.6.1 (2019-07-05)
Platform	x86_64-apple-darwin15.6.0 (64-bit)
Operating system	macOS Mojave 10.14.6
Packages	abind1.4-5, annotate1.64.0, AnnotationDbi1.48.0, ape5.4-1, askpass1.1, assertthat0.2.1, bibtex0.4.2.3, Biobase2.46.0, BiocFileCache1.10.2, BiocGenerics0.32.0, BiocManager1.30.10, BiocParallel1.20.1, biomaRt2.45.9, bit4.0.4, bit644.0.5, bitops1.0-6, blob1.2.1, cerebroApp1.2.2, cli2.1.0, cluster2.1.0, codetools0.2-16, colorspace1.4-1, colourpicker1.1.0, cowplot1.1.0, crayon1.3.4, curl4.3, data.table1.12.8, DBI1.1.0, dbplyr1.4.4, DelayedArray0.12.3, deldir0.1-29, digest0.6.26, dplyr1.0.2, DT0.16, ellipsis0.3.1, enrichR2.1, evaluate0.14, fansi0.4.1, farver2.0.3, fastmap1.0.1, fitdistrplus1.1-1, formattable0.2.0.1, future1.19.1, future.apply1.6.0, geneplotter1.64.0, generics0.0.2, GenomeInfoDb1.22.1, GenomeInfoDbData1.2.2, GenomicRanges1.38.0, ggplot23.3.2, ggrepel0.8.2, ggridges0.5.2, ggsci2.9, ggtree2.0.4, globals0.13.1, glue1.4.2, goftest1.2-2, graph1.64.0, gridExtra2.3, GSEABase1.48.0, GSVA1.34.0, gtable0.3.0, highr0.8, hms0.5.3, htmltools0.5.0, htmlwidgets1.5.2, httpuv1.5.4, httr1.4.2, ica1.0-2, igraph1.2.6, IRanges2.20.2, irlba2.3.3, jsonlite1.7.1, kableExtra1.2.1, KernSmooth2.23-17, knitcitations1.0.10, knitr1.31, labeling0.4.2, later1.1.0.1, lattice0.20-41, lazyeval0.2.2, leiden0.3.3, lifecycle0.2.0, limma3.42.2, listenv0.8.0, lmtest0.9-38, lubridate1.7.9, magrittr1.5, MASS7.3-53, MAST1.12.0, Matrix1.2-18, matrixStats0.57.0, memoise1.1.0, mgcv1.8-33, mime0.9, miniUI0.1.1.1, msigdbr7.2.1, munsell0.5.0, nlme3.1-149, openssl1.4.3, openxlsx4.2.2, patchwork1.0.1, pbapply1.4-3, pillar1.4.6, pkgconfig2.0.3, plotly4.9.2.1, plyr1.8.6, png0.1-7, polyclip1.10-0, prettyunits1.1.1, progress1.2.2, promises1.1.1, purrr0.3.4, qvalue2.18.0, R62.4.1, RANN2.6.1, rappdirs0.3.1, RColorBrewer1.1-2, Rcpp1.0.5, RcppAnnoy0.0.16, RCurl1.98-1.2, readr1.4.0, RefManageR1.2.12, reshape21.4.4, reticulate1.16, rjson0.2.20, rlang0.4.8, rmarkdown2.4, ROCR1.0-11, rpart4.1-15, RSpectra0.16-0, RSQLite2.2.1, rstudioapi0.11, rsvd1.0.3, Rtsne0.15, rvcheck0.1.8, rvest0.3.6, S4Vectors0.24.4, scales1.1.1, sctransform0.3.1.9002, sessioninfo1.1.1, Seurat3.9.9.9008, shiny1.5.0, shinydashboard0.7.1, shinythemes1.1.2, shinyWidgets0.5.4, SingleCellExperiment1.8.0, spatstat1.64-1, spatstat.data1.4-3, spatstat.utils1.17-0, stringi1.5.3, stringr1.4.0, SummarizedExperiment1.16.1, survival3.2-7, tensor1.5, tibble3.0.4, tidyr1.1.2, tidyselect1.1.0, tidytree0.3.3, treeio1.10.0, uwot0.1.8.9001, vctrs0.3.4, viridis0.5.1, viridisLite0.3.0, webshot0.5.2, withr2.3.0, xfun0.21, XML3.99-0.3, xml21.3.2, xtable1.8-4, XVector0.26.0, yaml2.2.1, zip2.1.1, zlibbioc1.32.0, zoo1.8-8

References

knitcitations::write.bibtex(file="references.bib")

Chen, Edward Y. 2020. “Enrichr.” . https://amp.pharm.mssm.edu/Enrichr/.

Gandolfo, Luke C., and Terence P. Speed. 2018. “RLE Plots: Visualizing Unwanted Variation in High Dimensional Data.” Edited by Enrique Hernandez-Lemus. PLOS ONE 13 (2). Public Library of Science (PLoS): e0191629. https://doi.org/10.1371/journal.pone.0191629.

Hafemeister, Christoph, and Rahul Satija. 2019. “Normalization and Variance Stabilization of Single-Cell RNA-Seq Data Using Regularized Negative Binomial Regression.” Genome Biology 20 (1). Springer Science; Business Media LLC. https://doi.org/10.1186/s13059-019-1874-1.

Liu, Fenglin, Yuanyuan Zhang, Lei Zhang, Ziyi Li, Qiao Fang, Ranran Gao, and Zemin Zhang. 2019. “Systematic Comparative Analysis of Single-Nucleotide Variant Detection Methods from Single-Cell RNA Sequencing Data.” Genome Biology 20 (1). Springer Science; Business Media LLC. https://doi.org/10.1186/s13059-019-1863-4.

romanhaa. 2021. “Romanhaa/cerebroApp.” GitHub. https://github.com/romanhaa/cerebroApp. https://github.com/romanhaa/cerebroApp.

“Satija Lab.” 2020. http://www.satijalab.org/seurat/v3.1/cell_cycle_vignette.html. https://satijalab.org/seurat/v3.1/cell_cycle_vignette.html.

Tirosh, I., B. Izar, S. M. Prakadan, M. H. Wadsworth, D. Treacy, J. J. Trombetta, A. Rotem, et al. 2016. “Dissecting the Multicellular Ecosystem of Metastatic Melanoma by Single-Cell RNA-Seq.” Science 352 (6282). American Association for the Advancement of Science (AAAS): 189–96. https://doi.org/10.1126/science.aad0501.

“Understanding Umap.” 2019. https://pair-code.github.io/understanding-umap/. https://pair-code.github.io/understanding-umap/.